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Record W4224914420 · doi:10.1002/ecs2.4011

Clustering community science data to infer songbird migratory connectivity in the Western Hemisphere

2022· article· en· W4224914420 on OpenAlexafffund
Jaimie G. Vincent, Richard Schuster, Scott Wilson, Daniel Fink, Joseph Bennett

Bibliographic record

VenueEcosphere · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsNature Conservancy of CanadaUniversity of Northern British ColumbiaEnvironment and Climate Change CanadaCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change Canada
KeywordsWarblerSongbirdEcologyPopulationBird migrationGeographyAbundance (ecology)BiologyData scienceHabitatComputer science

Abstract

fetched live from OpenAlex

Abstract Migratory connectivity describes the spatial linkage among migrating individuals through time. Accounting for it is necessary for full annual cycle conservation planning, to avoid uneven protection leading to overall population declines. However, conventional methods used to study migratory connectivity usually demand substantial fiscal and human resources. We present a methodology that infers patterns of migratory connectivity for songbirds using relative abundance models created from eBird, a global community science program. We compare our inferences with previously described patterns of migratory connectivity for two species assumed to exhibit broadscale parallel migration strategies: wood thrush ( Hylocichla mustelina ) and Wilson's warbler ( Cardellina pusilla ). Initial findings suggest that our method has the potential to be a rapid and inexpensive way to infer broad patterns of connectivity for species that do not engage in leapfrog migration nor deviate much from parallel migration. Our flexible framework can be used to guide sampling designs for studies of migratory connectivity and to generate hypotheses for species in need of urgent conservation planning for which migratory connectivity has not yet been established.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.053
GPT teacher head0.292
Teacher spread0.240 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2022
Admission routes2
Has abstractyes

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