MétaCan
Menu
Back to cohort
Record W4366428877 · doi:10.21203/rs.3.rs-2294878/v2

A collaborative and near-comprehensive North Pacific humpback whale photo-ID dataset

2023· preprint· en· W4366428877 on OpenAlexafffund
Ted Cheeseman, Ken Southerland, Jo Marie Acebes, Katherina Audley, Jay Barlow, Lars Bejder, Caitlin Birdsall, Amanda L. Bradford, Josie Byington, John Calambokidis, Rachel Cartwright, Jen Cedarleaf, Andrea Jacqueline García Chávez, Jens J. Currie, Joëlle De Weerdt, Nicole Doe, Thomas Doniol‐Valcroze, Karina Dracott, Olga A. Filatova, Rachel Finn, Kiirsten Flynn, John K. B. Ford, Astrid Frisch‐Jordán, Christine M. Gabriele, Beth Goodwin, Craig Hayslip, Jackie Hildering, Marie C. Hill, Jeff K. Jacobsen, Meagan Jones, Nozomi Kobayashi, Edward Lyman, Mark Malleson, Evgeny Mamaev, Pamela Martínez Loustalot, Annie Masterman, Craig O. Matkin, Christie J. McMillan, Jeff E. Moore, John R. Moran, Janet L. Neilson, Hayley Newell, Haruna Okabe, Marilia Olio, Adam A. Pack, Daniel M. Palacios, Heidi C. Pearson, Ester Quintana‐Rizzo, Raúl Fernando Ramírez Barragán, Nicola Ransome, Fred Sharpe, Tasli Shaw, Stephanie H. Stack, Iain J. Staniland, Janice M. Straley, Andrew Szabo, Suzie S Teerlink, Olga Titova, Jorge Urban R., Martin van Aswegen, Marcel Vinicius de Morais, Olga von Ziegesar, Briana Witteveen, Janie Wray, Kymberly Yano, Denny Zwiefelhofer, Hiram Rosales‐Nanduca, M. Esther Jiménez-López, Phil Clapham

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans Canada
FundersOffice of Naval ResearchFisheries and Oceans CanadaNational Oceanic and Atmospheric AdministrationParks CanadaStrongConsejo Nacional de Ciencia y TecnologíaU.S. Department of Defense
KeywordsHumpback whaleGeographyWhaleMarine mammalFisheryResource (disambiguation)Scale (ratio)Structural basinOceanographyCartographyComputer scienceBiologyGeology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.091
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0380.039

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 head0.092
GPT teacher head0.376
Teacher spread0.284 · 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 designNot applicable
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

Citations2
Published2023
Admission routes2
Has abstractno

Explore more

Same venueResearch SquareSame topicMarine animal studies overviewFrench-language works237,207