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Record W2609258959 · doi:10.1650/condor-16-116.1

Directional effects of biotic homogenization of bird communities in Mexican seasonal forests

2017· article· en· W2609258959 on OpenAlexfundno aff
Leopoldo D. Vázquez‐Reyes, Marı́a del Coro Arizmendi, Héctor Godínez–Álvarez, Adolfo G. Navarro‐Sigüenza

Bibliographic record

VenueOrnithological Applications · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAVMcGill University
KeywordsBiodiversitySpecies richnessDeciduousEcologyGeographyHomogenization (climate)EcosystemBiotic componentEnvironmental scienceAgroforestryAbiotic componentBiology

Abstract

fetched live from OpenAlex

Biotic homogenization—the erosion of biological differences between ecosystems owing to human perturbation—is a trait of the global biodiversity crisis that can affect tropical dry forest biodiversity. We tested whether biotic homogenization was occurring in resident forest bird communities in west-central Mexico. We conducted point-count surveys to assess biotic dissimilarity between resident bird communities in tropical deciduous and oak forests in the upper Balsas River basin across 3 levels of anthropogenic perturbation: primary forest, second-growth forest, and human settlements. We detected a reduction in species richness and taxonomic dissimilarity with increasing anthropogenic effects, due to a directional pattern in which lowland species expanded their elevational distributions up into oak forests. These results point to a need to change agricultural strategies to mitigate impacts on natural vegetation cover and biodiversity.

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)0.001
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.259
Teacher spread0.247 · 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

Citations42
Published2017
Admission routes1
Has abstractyes

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