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Record W4395567640 · doi:10.15468/4qsb75

Sargasso 2004 - Seabirds

2021· dataset· en· W4395567640 on OpenAlexaffabout
David Hyrenbach, Hal Whitehead

Bibliographic record

VenueGlobal Biodiversity Information Facility · 2021
Typedataset
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSargasso seaGeographyOceanographyEnvironmental scienceFisheryBiologyGeology

Abstract

fetched live from OpenAlex

Original provider: Dalhousie University Dataset credits: Hal Whitehead, Department of Biology, Dalhousie University Abstract: The Sargasso Sea cruise surveyed marine birds and mammals in the Northeast Atlantic Ocean, during the summer of 2004. This survey onboard the Valiant 40 ocean-going research vessel Balaena, consisted of two survey legs: a sail from Halifax, Nova Scotia to Beaufort, North Carolina (May 5 - 27), and a round trip from Beaufort, North Carolina to offshore waters north of the Bahamas (June 2 - 22). Seabirds were surveyed during the second leg of this cruise by Lesley Thorne, a graduate student from the Duke University Marine Laboratory.

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)0.003
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: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.214
Threshold uncertainty score0.425

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.008
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0630.043

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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
GenreDataset

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

Citations0
Published2021
Admission routes2
Has abstractyes

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