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Record W3201886883 · doi:10.3390/jmse9101057

The Biology of Mesopelagic Fishes and Their Catches (1950–2018) by Commercial and Experimental Fisheries

2021· article· en· W3201886883 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Marine Science and Engineering · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsTula FoundationUniversity of British ColumbiaFisheries and Oceans Canada
Fundersnot available
KeywordsMesopelagic zoneBycatchFisheryDiscardsFisheries scienceTrawlingFisheries managementFish <Actinopterygii>FishingPelagic zoneBiology

Abstract

fetched live from OpenAlex

Following a brief review of their biology, this contribution is an attempt to provide a global overview of the catches of mesopelagic fishes (of which 2.68 million tonnes were officially reported to the FAO) throughout the world ocean from 1950 to 2018, to serve as a baseline to a future development of these fisheries. The overview is based on a thorough scanning of the literature dealing with commercial or experimental fisheries for mesopelagics and their catches, and/or the mesopelagic bycatch of other fisheries. All commercial (industrial and artisanal) fisheries for mesopelagic fishes were included, as well as experimental fisheries of which we were aware, while catches performed only to obtain scientific samples were omitted. The processes of generating bycatch and causing discards are discussed, with emphasis on Russian fisheries. From peer-reviewed and gray literature, we lifted information on mesopelagic fisheries and assembled it into one document, which we then summarized into two text tables with catch data, one by country/region, the other by species or species groups.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.133

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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