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Record W4387574170 · doi:10.7755/mfr.84.3-4.1

Morphometric Conversions for 33 Shark Species from the Western North Atlantic Ocean

2023· article· en· W4387574170 on OpenAlexaffabout
Lisa J. Natanson, Camilla T. McCandless, Michelle S. Passerotti, Carolyn Belcher, Heather D. Bowlby, William B. Driggers, Bryan S. Frazier, James Gelsleichter, Simon J. B. Gulak, Jill M. Hendon, Eric R. Hoffmayer, Warren Joyce

Bibliographic record

VenueMarine Fisheries Review · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsBedford Institute of Oceanography
Fundersnot available
KeywordsOceanographyGeographyFisheryBiologyGeology

Abstract

fetched live from OpenAlex

This study generates and updates mathematical conversions among body length and weight measurements of sharks commonly encountered in the west-ern North Atlantic Ocean. At the initiation of individual research programs, stan-dardized measurements are determined to meet program objectives, yet these mea-surements often vary among programs and may differ within programs over time. Since length is of vital importance to understanding the basic biology of a species (e.g., growth, length at maturity) and to enforce management regulations based on size, it is necessary to have length-length and length-weight conversions to be able to standardize measurements for individu-al species. We compiled length and weight data on sharks from nine research pro-grams operating in the western North Atlantic Ocean from Canada through the Gulf of Mexico to obtain length-length and length-weight conversions for 27 spe-cies and 3 genera consisting of 6 species. Length-length and length-weight conver-sions are presented for all species using over the body fork length as the indepen-dent variable. This study updates and ex-pands previous conversions with new in-formation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.003

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 head0.027
GPT teacher head0.231
Teacher spread0.204 · 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; both teacher heads agree on what is shown here.

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

Citations7
Published2023
Admission routes2
Has abstractyes

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