1,062 Matching Annotations
  1. Mar 2017
    1. Canadian Wildlife Service

      The Canadian Wildlife Service organization was originally founded under the name of the Dominion Wildlife Service in November 1947. There were about thirty staff members of the organization at this time. In 1950, the organization’s name was changed to its current title of the Canadian Wildlife Service. The three main focuses of the Canadian Wildlife Service have been and continue to be the management of migratory birds, the management of game and furbearing mammals, and the enforcement of international treaties to ensure conservation of species. In order to accomplish these tasks, the Canadian Wildlife Service has conducted extensive research regarding population, population ecology, survival factors, migration patterns, limnological studies, environmental toxicology, and endangered species evaluation and protection of several species of the Arctic. Examples of these species include elk, moose, bison, caribou, muskoxen, polar bears, wolves, arctic foxes, geese, ducks, songbirds, seabirds, trumpeter swans, whooping cranes, and peregrine falcons. Additionally, the Canadian Wildlife Service has been tasked with the management of National Parks and the creation of public education programs (Burnett et al. 1999).

      During the 1970s, the Canadian Wildlife Service researched and reported on the reproductive success of the black-crowned night heron on Pigeon Island of Lake Ontario (Price 1978), biology of the Kaminuriak population of barren-ground caribou (Arctic 1977), hunting of and attacks by polar bears along the Manitoba coast of Hudson Bay (Jonkel et al. 1976), biology and management of bears (Bears: Their Biology and Management 1976), and many other environmental and biological concerns regarding the wildlife of the Arctic.

      Additional information and the current contact information of the Canadian Wildlife Service can be found at: https://www.ec.gc.ca/paom-itmb/default.asp?lang=En&n=5f569149-1.

      References

      "Books Received." Arctic 30, no. 1 (1977): 67-68.<br> http://www.jstor.org/stable/40508780.

      Burnett, J. A., and Canadian Wildlife Service. 1999. A Passion for Wildlife: A History of the Canadian Wildlife Service, 1947-1997 and Selected Publications from Work by the Canadian Wildlife Service. Canadian field-naturalist, v. 113, no. 1; Canadian field-naturalist, v. 113, no. 1.

      Jonkel, Charles, Ian Stirling, and Richard Robertson. "The Popular Bears of Cape Churchill." Bears: Their Biology and Management 3 (1976): 301-02. doi:10.2307/3872777.

      "Preface." Bears: Their Biology and Management 3 (1976): 7. http://www.jstor.org/stable/3872749.

      Price, Iola. "Black-Crowned Night Heron Reproductive Success on Pigeon Island, Lake Ontario 1972- 1977 (Abstract Only)." Proceedings of the Colonial Waterbird Group 1 (1978): 166. doi:10.2307/1520916.

  2. Feb 2017
    1. The project plan is at best an educated guess. An educated guess that can be improved upon by capitalizing on the expertise of others that have been previously involved in projects of this nature. And yet there are many projects for which there are exists no similar requisite expertise. As such, the project plan remains very much simply ones conscientious best guess at what needs to be done.

      I believe here is where fear creeps in, since nobody in the team has encountered a situation with that problem set before. They literally have nothing to fall back on, no past knowledge or experience, they have to create knowledge.

  3. Dec 2016
    1. Our abilities to make observations are limited to a small range of space and time scales (8), limiting our capacity for understanding ecosystems and forecasting how they will respond to local and global change.

      Our abilities to manage natural systems are also typically limited to a small range of space and time scales.

    2. A range of information sources, which can include models, is used to develop alternative plausible trajectories of ecosystems; uncertainties about the future are represented by the range of conditions captured by the ensemble of scenarios. In contrast, forecasts narrowly limit uncertainties to those associated with a single potential outcome that is assumed to be predictable

      This strong distinction between "forecasts" and "scenarios" seems like a rather arbitrary distinction on the surface. There are forecasting approaches that attempt to account for uncertainty in a broad array of things including uncertainty in the generating model. Many of the examples in Principles of Forecasting by J. Scott Armstrong are what would be described as "scenario" based approaches here. Likewise some of the approaches employed by forecasters in Superforecasting by Tetlock & Gardner involve developing a range of scenarios.

      Scenarios in general need to have a reasonable probability of occurrence to be usefully included in decision making. So at least at some minimum threshold it a probability is being associated with scenarios. Going one step further and assigning a probability to each member of a set of scenarios would result in a probabilistic forecast.

      In short, it seems to me that scenario development is, in many cases, a kind of forecasting. It may involve large uncertainties and it may currently be associated with different kinds of decision making, like choosing management practices that are robust to may possible models, but these can both be accomplished in other ways. Using language that implies that these are completely distinct approaches seems likely to cause confusion and unnecessary terminological debate.

  4. Nov 2016
  5. Sep 2016
    1. A recent Hewlett-Packard printer software update changed the printers so they would not work with third-party ink cartridges. Worse, the change was made as part of a security update.

      https://act.eff.org/action/tell-hp-say-no-to-drm Petition HP to fix this wrongdoing, and promise not to repeat it. They are also being asked to promise not to invoke the DMCA against security researchers who find vulnerabilities in their products.

    1. frame the purposes and value of education in purely economic terms

      Sign of the times? One part is about economics as the discipline of decision-making. Economists often claim that their work is about any risk/benefit analysis and isn’t purely about money. But the whole thing is still about “resources” or “exchange value”, in one way or another. So, it could be undue influence from this way of thinking. A second part is that, as this piece made clear at the onset, “education is big business”. In some ways, “education” is mostly a term for a sector or market. Schooling, Higher Education, Teaching, and Learning are all related. Corporate training may not belong to the same sector even though many of the aforementioned EdTech players bet big on this. So there’s a logic to focus on the money involved in “education”. Has little to do with learning experiences, but it’s an entrenched system.

      Finally, there’s something about efficiency, regardless of effectiveness. It’s somewhat related to economics, but it’s often at a much shallower level. The kind of “your tax dollars at work” thinking which is so common in the United States. “It’s the economy, silly!”

  6. Aug 2016
    1. Time management tips from Quincy Larson.

      • Keep a simple to-do list.
      • If you can do something in a few minutes, do it now. Otherwise, add it to your to-do list.
      • Avoid unnecessary meetings, and even video chat. Prefer email or other asynchronous channels.
      • Listen to podcasts and audiobooks while you exercise.
  7. Jul 2016
    1. Both sides are wrong — Yiannopoulos is no free-speech martyr, and cheerleaders of the ban are likely fooling themselves if they interpret this as any sort of sign of evolving Twitter policy rather than a specific instance of damage control that’s unlikely to lead to wider reforms.
  8. May 2016
    1. Are these really alternatives to fertilizers? I think not. Although these adaptations may help improve nutrient use efficiency of crops (that amount of the nutrient pool in the soil that crops take up), aside from legume nodules they fail as fertilizer alternatives due to conservation of mass, which here can be stated as “nutrients exported from a field must be replaced by an equal import of nutrients.” Nutrients are not created in the field through any mechanism, natural or not. Even nitrogen from legumes is imported from the air. None of these so-called alternatives to synthetic fertilizers create nutrients. They exist to help plants survive (not thrive) in the nutrient limited conditions found in natural ecosystems. Since farmers strive to eliminate such nutrient limitations in their fields, these mechanisms are not so helpful, and they are often switched off when high levels of nutrients are available.
  9. Apr 2016
    1. choose to invite Hypothesis annotators by embedding our client.

      And even setting things up so that thoughtful commentary is specifically encouraged. The tool may be part of it but the key difference, in my own personal experience, is about the first few interactions. When @RemiHolden invites annotations to his blogposts about annotations, he does so in the context of a burgeoning community of practice around open annotations for pedagogy. Much closer to the climate science case and, interestingly, quite close to the very memos and Requests for Comments at the origin of the Internet.

    1. Manure markets tend to be highly localized. In some areas, manure carries enough value as fertilizer that crop producers are willing to pay to receive it; in other areas, livestock producers must pay other farmers to take the manure. About 20 percent of the dairy and hog manure that is removed from farms is sold, as is 36 percent of broiler litter.17 About 60 percent of the hog and broiler manure that is removed from farms is given away for no exchange of money. Prices for manure are determined by the quantities produced in an area relative to the amount of nearby cropland, the mix of crops grown, and the cost of transporting manure. With production shifting to large livestock operations, which are coming under increasing pressure to reduce nutrient applications to their own land, we can expect to see increased manure removals
    1. Conclusions liquid hog manure application was the largest user of energy in this study, mainly due to fuel consumption the most energy efficient system in this study was the grazing system with no manure applied grazing systems were more energy efficient than hayed systems beef production on unmanured land was more energy efficient than on manured land however, beef production on manured land is still more efficient than beef production on land where synthetic fertilizers are applied, even if manure is applied only to P-removal rates
    1. What recommendations do you have for platforms like Genius and Hypothesis to manage (the potential for) abuse?

      Yes, plenty. Most of them have little to do with the platforms, at a technical level. But they do have a whole lot to do with their userbase. As Trapani says, “your community is your best feature”.

  10. Mar 2016
  11. Feb 2016
    1. But community must not mean a shedding of our differences, nor the pathetic pretense that these differences do not exist

      What a great, simple critique of bullshit "solidarity" cries.

      Of course, it raises for me feelings of discomfort because I've observed that even those who frequently profess to value difference within a community often still believe it important that the community present a unified face when perceived by outside groups.

      Even within a single company, this sort of philosophy manifests frequently as executives fighting viciously with one another while smiling and acting as though they are all of one mind when presenting to the rest of the company.

  12. Jan 2016
  13. Dec 2015
  14. Nov 2015
    1. Bureaucratic cultures tend to discourage people from speaking candidly. Lack of candor can be a deterrent to success, before it ever reaches the level of outright lies. Lack of candor means:

      • outright lies (saying something you know to be false)
      • self-deception (believing what you want to believe)
      • deliberate omissions of facts
      • thinking one thing, but saying something different
      • having an idea that may be of value, but saying nothing
      • being called upon to give an honest opinion, but deciding to say what is easier, or what you think others want to hear
      • obscure jargon, or meaningless platitudes that give the impression everything is going fine or great. (This is a big red flag when it appears in corporate reports.)

      "Investing Between the Lines: How to Make Smarter Decisions by Decoding CEO Communications", L.J. Rittenhouse (recommended by Warren Buffet in his 2012 Shareholder Letter)

      Truth-Telling: Confronting the Reality of the Lack of Candor Inside Organizations We need to build cultures where "opposing views are debated and more effective solutions and innovations are created." -- Lynn Harris

    1. user innovation toolkit - a product malleable enough to let users adapt it to their own needs.

      Trello is a project management tool that provides boards, lists, and cards. The cards represent tasks or items, and move across columns on the board as they progress to a new stage of development. No particular method is prescribed. The individual or team decides how to use Trello, and the method is likely to evolve. Different projects may require different methods.

      Trello has an API to allow automation and customization. After agreeing on how to use the board, different team members might use the API to build interfaces that work best for them.

  15. Oct 2015
  16. Sep 2015
  17. Jun 2015
  18. May 2015
    1. Fundamental questions for the library revolve around issues of: stewardship (what types of annotations are appropriate for library ownership, vs. say a course platform), persistence (how long should different types of annotations be persisted and preserved), costs (who will fund annotation storage over time) access (what privacy and distribution controls need to be placed on access to annotations.)
  19. Sep 2014
  20. Feb 2014
    1. API Management Using Github

      I have documented eleven approaches to using Github for API management to date:

      • Design and Code
      • Documentation
      • Software development kits (SDK)
      • Code Samples (Gists)
      • Developer Authentication
      • Developer Profiling
      • Presentations and Guides
      • Issue Management
      • Roadmaps
      • Hackathons
      • Terms of Service, Privacy, and Branding
    1. API Services During my monitoring of the API space, I came across a new API monitoring service called AutoDevBot, which monitors all your API endpoints, and notifies you when something goes wrong. Pretty standard feature in a new wave of API integration tools and services I’m seeing emerge, but what is interesting is they use Github as a central place to store the settings for the API monitoring service. AutoDevBot has you clone their settings template, make changes you need to monitor your APIs, register and fire up AutoDevBot to monitor. Seems like a pretty simple way for API service providers to engage with API providers, allowing them to manage all the configuration for API services alongside their own internal API operations.
    2. Github As The Central Presence, Definition, Configuration, And Source Code For Your API Posted on 02-05-2014 It is easy to think of Github as a central repository for your open source code—most developers understand that. I have written before about the many ways to use Github as part of your API management strategy, but in the last few months I'm really seeing Github playing more of a central role in the overall lifecycle of an API.
  21. Jan 2014
    1. Journals and sponsors want you to share your data

      What is the sharing standard? What are the consequences of not sharing? What is the enforcement mechanism?

      There are three primary sharing mechanisms I can think of today: email, usb stick, and dropbox (née ftp).

      The dropbox option is supplanting ftp which comes from another era, but still satisfies an important niche for larger data sets and/or higher-volume or anonymous traffic.

      Dropbox, email and usb are all easily accessible parts of the day-to-day consumer workflow; they are all trivial to set up without institutional support or, importantly, permission.

      An email account is already provisioned by default for everyone or, if the institutional email offerings are not sufficient, a person may easily set up a 3rd-party email account with no permission or hassle.

      Data management alternatives to these three options will have slow or no adoption until the barriers to access and use are as low as email; the cost of entry needs to be no more than *a web browser, an email address, and no special permission required".

    2. An effective data management program would enable a user 20 years or longer in the future to discover , access , understand, and use particular data [ 3 ]. This primer summarizes the elements of a data management program that would satisfy this 20-year rule and are necessary to prevent data entropy .

      Who cares most about the 20-year rule? This is an ideal that appeals to some, but in practice even the most zealous adherents can't picture what this looks like in some concrete way-- except in the most traditional ways: physical paper journals in libraries are tangible examples of the 20-year rule.

      Until we have a digital equivalent for data I don't blame people looking for tenure or jobs for not caring about this ideal if we can't provide a clear picture of how to achieve this widely at an institutional level. For digital materials I think the picture people have in their minds is of tape backup. Maybe this is generational? New generations not exposed widely to cassette tapes, DVDs, and other physical media that "old people" remember, only then will it be possible to have a new ideal that people can see in their minds-eye.

    3. A key component of data management is the comprehensive description of the data and contextual information that future researchers need to understand and use the data. This description is particularly important because the natural tendency is for the information content of a data set or database to undergo entropy over time (i.e. data entropy ), ultimately becoming meaningless to scientists and others [ 2 ].

      I agree with the key component mentioned here, but I feel the term data entropy is an unhelpful crutch.

    1. Data management activities, grouped. The data management activities mentioned by the survey can be grouped into five broader categories: "storage" (comprising backup or archival data storage, identifying appropriate data repositories, day-to-day data storage, and interacting with data repositories); "more information" (comprising obtaining more information about curation best practices and identifying appropriate data registries and search portals); "metadata" (comprising assigning permanent identifiers to data, creating and publishing descriptions of data, and capturing computational provenance); "funding" (identifying funding sources for curation support); and "planning" (creating data management plans at proposal time). When the survey results are thus categorized, the dominance of storage is clear, with over 80% of respondents requesting some type of storage-related help. (This number may also reflect a general equating of curation with storage on the part of respondents.) Slightly fewer than 50% of respondents requested help related to metadata, a result explored in more detail below.

      Categories of data management activities:

      • storage
        • backup/archival data storage
        • identifying appropriate data repositories
        • day-to-day data storage
        • interacting with data repositories
      • more information
        • obtaining more information about curation best practices
        • identifying appropriate data registries
        • search portals
      • metadata
        • assigning permanent identifiers to data
        • creating/publishing descriptions of data
        • capturing computational provenance
      • funding
        • identifying funding sources for curation support
      • planning
        • creating data management plans at proposal time
    1. Having made these points many times in the last few years, I've realized that the fundamental problem is in the mistaken belief that the type system has anything whatsoever to do with the storage allocation strategy. It is simply false that the choice of whether to use the stack or the heap has anything fundamentally to do with the type of the thing being stored. The truth is: the choice of allocation mechanism has to do only with the known required lifetime of the storage.

      The type system has nothing to do with the storage allocation strategy; the choice of allocation mechanism has to do only with the known required lifetime of the storage.

    1. Now compare this to the stack. The stack is like the heap in that it is a big block of memory with a “high water mark”. But what makes it a “stack” is that the memory on the bottom of the stack always lives longer than the memory on the top of the stack; the stack is strictly ordered. The objects that are going to die first are on the top, the objects that are going to die last are on the bottom. And with that guarantee, we know that the stack will never have holes, and therefore will not need compacting. We know that the stack memory will always be “freed” from the top, and therefore do not need a free list. We know that anything low-down on the stack is guaranteed alive, and so we do not need to mark or sweep.
    2. When a garbage collection is performed there are three phases: mark, sweep and compact. In the “mark” phase, we assume that everything in the heap is “dead”. The CLR knows what objects were “guaranteed alive” when the collection started, so those guys are marked as alive. Everything they refer to is marked as alive, and so on, until the transitive closure of live objects are all marked. In the “sweep” phase, all the dead objects are turned into holes. In the “compact” phase, the block is reorganized so that it is one contiguous block of live memory, free of holes.
    3. If we’re in that situation when new memory is allocated then the “high water mark” is bumped up, eating up some of the previously “free” portion of the block. The newly-reserved memory is then usable for the reference type instance that has just been allocated. That is extremely cheap; just a single pointer move, plus zeroing out the newly reserved memory if necessary.
    4. The idea is that there is a large block of memory reserved for instances of reference types. This block of memory can have “holes” – some of the memory is associated with “live” objects, and some of the memory is free for use by newly created objects. Ideally though we want to have all the allocated memory bunched together and a large section of “free” memory at the top.
    1. Despite her work ethic, her track record, and the fact that we all really liked her, her skills were no longer adequate. Some of us talked about jury-rigging a new role for her, but we decided that wouldn’t be right. So I sat down with Laura and explained the situation—and said that in light of her spectacular service, we would give her a spectacular severance package. I’d braced myself for tears or histrionics, but Laura reacted well: She was sad to be leaving but recognized that the generous severance would let her regroup, retrain, and find a new career path. This incident helped us create the other vital element of our talent management philosophy: If we wanted only “A” players on our team, we had to be willing to let go of people whose skills no longer fit, no matter how valuable their contributions had once been. Out of fairness to such people—and, frankly, to help us overcome our discomfort with discharging them—we learned to offer rich severance packages.
  22. Dec 2013
    1. I do not maintain any big open source projects, but in talking to people who do it’s become my understanding that the bulk of the work is sifting through issues and pull requests, not actually coding. The former is the thing they consider hardest, the thing that burns them out, their most overwhelming responsibility.