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Record W3092143966 · doi:10.1038/s41597-020-00676-y

A salmon diet database for the North Pacific Ocean

2020· article· en· W3092143966 on OpenAlexafffund
Caroline Graham, Evgeny A. Pakhomov, Brian P. V. Hunt

Bibliographic record

VenueScientific Data · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersHakai InstituteUniversity of British ColumbiaG. Unger Vetlesen FoundationAmbrose Monell Foundation
KeywordsPacific oceanOceanographyFisheryGeographyBiologyGeology

Abstract

fetched live from OpenAlex

The North Pacific Marine Salmon Diet Database is an open-access relational database built to centralize and make accessible salmon diet data through a standardized database structure. The initial data contribution contains 21,862 observations of salmon diet, and associated salmon biological parameters, prey biological parameters, and environmental data from the North Pacific Ocean. The data come from 907 unique spatial areas and mostly fall within two time periods, 1959-1969 and 1987-1997, during which there are more data available compared to other time periods. Data were extracted from 62 sources identified through a systematic literature review, targeting peer-reviewed and gray literature. The purpose of this database is to consolidate data into a common format to address gaps in our ecological understanding of the North Pacific Ocean, particularly with respect to salmon. This database can be used to address a variety of questions regarding salmon foraging, productivity, and marine survival. The North Pacific Marine Salmon Diet Database will continue to grow in the future as more data are digitized and become available.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.745
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0030.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.083
GPT teacher head0.284
Teacher spread0.201 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations11
Published2020
Admission routes2
Has abstractyes

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