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Record W3084644403 · doi:10.1111/conl.12763

Avian cultural services peak in tropical wet forests

2020· article· en· W3084644403 on OpenAlexafffund
Alejandra Echeverri, Daniel S. Karp, Luke O. Frishkoff, Jaya Krishnan, Robin Naidoo, Jiaying Zhao, Jim Zook, Kai M. A. Chan

Bibliographic record

VenueConservation Letters · 2020
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsUniversity of British ColumbiaGovernment of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaU.S. Forest ServiceNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaUniversity of British Columbia Graduate SchoolKillam TrustsNational Geographic Society
KeywordsGeographyBiodiversitySociocultural evolutionWildlifeHabitatEcologyCitizen scienceClimate changeEnvironmental resource managementBiologyEnvironmental sciencePolitical science

Abstract

fetched live from OpenAlex

Abstract The current biodiversity crisis involves major shifts in biological communities at local and regional scales. The consequences for Earth's life‐support systems are increasingly well‐studied, but knowledge of how community shifts affect cultural services associated with wildlife lags behind. We integrated bird census data (3 years across 150 point‐count locations) with questionnaire surveys (>400 people) to evaluate changes in culturally important species across climate and land‐use gradients in Costa Rica. For farmers, urbanites, and birdwatchers alike, species valued for identity, bequest, birdwatching, acoustic aesthetics, and education were more likely to occupy wetter regions and forested sites, whereas disliked species tended to occupy drier and deforested sites. These results suggest that regional climate drying and habitat conversion in the Neotropics are likely to threaten the most culturally important bird species. This study provides a novel and generalizable pathway for assessing the effects of environmental changes on cultural services and integrating the sociocultural and ecological dimensions of 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 score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.303
Teacher spread0.256 · 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

Citations29
Published2020
Admission routes2
Has abstractyes

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