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Record W4386692111 · doi:10.1016/j.oneear.2023.08.017

Prioritizing global conservation of migratory birds over their migration network

2023· article· en· W4386692111 on OpenAlexaff
Wenyuan Zhang, Jie Wei, Yanjie Xu

Bibliographic record

VenueOne Earth · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsMcGill University
FundersChina Scholarship Council
KeywordsGeographyBird migrationEconomic geographyEnvironmental resource managementEcologyBiologyEnvironmental science

Abstract

fetched live from OpenAlex

Halting and reversing biodiversity loss is a grand challenge in the Anthropocene, which suggests an urgent need to effectively protect key areas that support species sustainability. However, large knowledge gaps exist in determining those key areas for migratory species and the extent to which they are protected, albeit with the essential and indispensable functions that migratory species perform in biodiversity conservation. Here, we used over 390 million community-contributed bird observations to derive order-specific, spatially explicit estimates of annual migration networks for 26 bird orders across the world. We found that 35% of the overall 343 important sites that strongly connect the migration network across the annual cycle of global migratory birds are uncovered by protected areas. This leads to nearly 87% of 1,862 migratory bird species being at risk. Migratory species benefit more from considering various levels of site importance to safeguard network integrity, with conservation efforts across countries.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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

Citations32
Published2023
Admission routes1
Has abstractyes

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