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Distribución y abundancia de las aves playeras en planos intermareales del Parque Nacional Natural Sanquianga y la bocana de Iscuandé, Nariño (Colombia), entre 2009 y 2020

2020· article· es· W3116010039 on OpenAlexfundno aff
Richard J. Gonzalez, Diana Eusse‐González, Natasha Valencia

Bibliographic record

VenueBoletín de Investigaciones Marinas y Costeras · 2020
Typearticle
Languagees
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersSimon Fraser University
KeywordsHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

11 % de las aves playeras del corredor migratorio del Pacífico americano están disminuyendo y otro 46 % no cuenta con información para estimar su estado poblacional. Para entender la magnitud y las causas de estos cambios, se requiere información de sitios de concentración como la bocana de Iscuandé (IS) y el Parque Nacional Natural Sanquianga (PNNS). Para caracterizar la composición de las comunidades de aves playeras en estos sitios, se analizaron diez años de conteo. A partir de la abundancia proporcional, la prevalencia y la densidad media de cada localidad, se evaluó si existían diferencias entre sitios con diferente influencia marina y del río Patía. IS aportó el 63 % de la abundancia promedio, siendo el 80 % aves playeras pequeñas. En PNNS, la abundancia se repartió 40-60 % entre aves grandes y pequeñas, y la contribución de cada bocana fue proporcional al área muestreada. Estos resultados muestran que los dos sitios tienen comunidades diferentes de aves playeras. Esta heterogeneidad espacial tiene importantes implicaciones ecológicas y de conservación. El recambio en la composición taxonómica y por grupos de tamaño de aves playeras sugiere diferencias en el hábitat intermareal y en los organismos que lo habitan, lo que ofrece alternativas para la conservación de diferentes especies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.792

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.231
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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