7,115 Matching Annotations
  1. Oct 2023
    1. Fediverso

      Implica que desde cualquier cuenta puedas seguir a cuentas de otros servicios sociales, es crear infraestructura para colaborar, no para competir

  2. Sep 2023
    1. Conspicuous colors (e.g., red, yellow, blue) have evolved numerous times across animals. But the function of this coloration can differ radically among species. Many species use this coloration as a sexual signal to conspecifics, whereas others use it as a warning signal to predators. Why do different species evolve conspicuous coloration in association with one function as opposed to the other?

      Los colores llamativos (p. ej., rojo, amarillo, azul) han evolucionado en numerosas ocasiones entre los animales. Pero la función de esta coloración puede diferir radicalmente entre especies. Muchas especies utilizan esta coloración como señal sexual para sus congéneres, mientras que otras la utilizan como señal de advertencia para los depredadores. ¿Por qué diferentes especies desarrollan una coloración llamativa en asociación con una función y no con la otra?

    2. Using phylogenetic logistic regression, we found that conspicuous, sexually dimorphic coloration is significantly associated with diurnal lineages (e.g., many birds and lizards). By contrast, the evolution of warning signals was significantly associated with large-scale clades that were ancestrally nocturnal (e.g., snakes, amphibians), regardless of the current diel activity of species. Overall, we show that the evolution of conspicuous coloration as warning signals or sexual signals is influenced by the ecology of species, both recently and in the ancient past.

      Utilizando regresión logística filogenética, encontramos que la coloración llamativa y sexualmente dimórfica está significativamente asociada con linajes diurnos (por ejemplo, muchas aves y lagartos). Por el contrario, La evolución de las señales de advertencia se asoció significativamente con clados a gran escala que eran ancestralmente nocturnos (p. ej., serpientes, anfibios), independientemente de la actividad diaria actual de las especies. En general, mostramos que la evolución de la coloración llamativa como señales de advertencia o señales sexuales está influenciada por la ecología de las especies, tanto recientemente como en el pasado antiguo.

      VertebradosBIOocolores

    1. Descripción Este bot genera una liga de hilos en mastodon para que sean leídos fácilmente

      Funcionamiento Mencionar @mastoreaderio(@mastodon.social) en un hilo y escribir "unroll" en cualquiera de los mensajes

      Ejemplos

      https://mstdn.social/@lma/111092262680767203

      https://mastoreader.io/?url=https%3A%2F%2Fmstdn.social%2F%40lma%2F111092262680767203

      Proyectos

      BIOmastodonte

  3. citation-js.toolforge.org citation-js.toolforge.org
    1. Descripción: Esta aplicación permite generar referencias bibliográficas utilizando literatura de Wikidata

      Formato para poner las citas https://citation-js.toolforge.org/api/v1/export/Q30000000,Q30000002/bibliography?format=html

      Reses Sociales https://mstdn.social/@citationjs@fosstodon.org

    1. 🔖Características

      Tipo:💻 Software

      Función:🛠️ recuperación y análisis de citas académicas

      Acceso: 🆓libre

      Entidad: 📢Proyecto individual

      Favorito:⭐⭐⭐

      🐦Tiene twitter @AWHarzing


      Twitter

      https://twitter.com/AWHarzing

      @AWHarzing

      https://twitter.com/hashtag/Kit_pop?src=hashtag_click


      Indicadores

      1. Número total de artículos y número total de citas
      2. Citas promedio por artículo, citas por autor, artículos por autor y citas por año
      3. Índice h de Hirsch y parámetros relacionados
      4. Índice g de Egghe
      5. Índice h contemporáneo
      6. Tres variaciones de índices h individuales
      7. Aumento anual promedio en el índice h individual
      8. Tasa de citas ponderada por edad
      9. Análisis del número de autores por artículo

      Catálogo force11: https://www.force11.org/node/4656

  4. Aug 2023
    1. MariposasBIOcolores

      ModeladoAlasMariposa

      SinAcceso

      LepidopteraBIocolores

      PlanEstructural

      Seminal

      Este es un buen ejemplo para hacer análisis de citas

    1. BIOnombres

      Synopsis Scientific names are critical metadata elements in biodiversity. They are the scaffolding upon which all biological information hangs. However, scientific names are imperfect identifiers. Some taxa share the same name (e.g. homonyms across nomenclature codes) and there can be many names for the same taxon. Names change because of taxonomic and nomenclatural revisions and they can be persistently misspelled in the literature. Optical scanning of printed material compounds the problem by introducing greater uncertainty in data integration.

      This resolution service tries to answer the following questions about a string representing a scientific name:

      Is this a name? It is spelled correctly? Is this name currently in use? What other names are related to this name (e.g. synonyms, lexical variants)? If this name is a homonym, which is the correct one? Matching Process 1. Exact Matching Submitted names are checked for exact matches against names in specified data sources or against the entire resolver database. If "resolve_once" is specified in the API, found names are immediately removed from the process instead of being resolved against all specified data sources. This significantly accelerates matching and can be used to discover if a string is in fact a name.

      1. Exact Matching of Canonical Forms Name strings are often supplied with complex authorship information [e.g. Racomitrium canescens f. epilosum (H. Müll. ex Milde) G. Jones in Grout]. The Global Name parser strips authorship and rank information from names [e.g. Racomitrium canescens epilosum], which makes it possible to compare the string with other variants of the same name. Resulting canonical forms are checked for exact matches against canonical forms in specified data sources or in the entire resolver database. All found names are removed from the process at the completion of this step.

      2. Fuzzy Matching of Canonical Forms Mistakes, misspellings, or OCR errors can create incorrect variants of scientific names. Remaining canonical forms generated from the previous step are fuzzily matched against canonical forms in specified data sources. We use a modified version of the TaxaMatch algorithm developed by Tony Rees. After this step all found names are removed from the process.

      3. Exact Matching of Specific Parts of Names Some names are recognized by the parser as infraspecific names, which were not found during previous steps of the process. This may be because the name is unknown to the resolver database. Sometimes a 'junk' word is wrongly included and the parser may recognize it as an infraspecific epithet. The algorithm extracts specific canonical forms from names recognized as infraspecific and tries to match this subset of names against datasources or the entire resolver database. For example, "Pardosa moesta spider" will be cleansed and matched as "Pardosa moesta". All found names are removed from the process prior to proceeding to the following steps.

      4. Fuzzy Matching of Specific Parts of Names Remaining names to be processed are fuzzily matched then removed.

      5. Exact Matching of Genus Part of Names Remaining names in the process as well as all remaining binomial canonical forms are reduced to the genus part and matched against the data sources or the entire database.

      Taxonomic Context If the "with_context" parameter is set to "true", the overall taxonomic group of all matched names is collected throughout the process. Scores for possible homonym matches are down-weighted if the resolved names do not belong to the overall taxonomic group of the queried list. If this is undesirable behavior, this parameter may be set to "false".

      Confidence Score Matched names fall into several categories. For example, if the name Aotus gets perfectly matched as a plant genus, this may be incorrect if the queried name actually refers to a genus of monkey. Another example is poor fuzzy matching. The name Afina can be fuzzily matched to the genus Alina in the Order Lepidoptera. Matches of trinomial or binomial names have greater accuracy. Matching of authorship information further increases the likelihood of a correct match. However, different authorship does not always mean different taxonomic meaning. For example, Monochamus galloprovincialis (Olivier, 1795) and Monochamus galloprovincialis Secchi, 1998 both refer to the same species, where the former indicates the original author of the name and the latter is merely a reference to the name. The name resolver produces a "confidence score" to accommodate all these potential issues. The score is produced from a curvilinear plot of weighted decisions.

      Confidence Score Graph We start at 0 on the x-axis and assign positive values for events that increase the probability score, and negative values to events that decrease it. For example, an exact match of a binomial name increases the probability significantly, so we adjust the slider 3 points to the right with a corresponding score of 0.988. However, if the authorship of the name did not get correctly matched, we adjust the slider 2 points to the left, to a corresponding score of 0.75. We try to map confidence level the with resulting scores. For example, 0.5 means neutral confidence whereas 0.99 mean high confidence.

      Global Names Architecture| Global Names Recognition & Discovery| Global Names Integrated Taxonomic Editor