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Record W3188461202 · doi:10.1111/mms.12855

Body condition index in beluga whale (<scp><i>Delphinapterus leucas</i></scp>) carcasses derived from morphometric measurements

2021· article· en· W3188461202 on OpenAlexafffund
Sylvain Larrat, Stéphane Lair

Bibliographic record

VenueMarine Mammal Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversité de Montréal
FundersFisheries and Oceans CanadaCanadian Wildlife Health CooperativeParks Canada
KeywordsBeluga WhaleBelugaLeucasCetaceaCondition indexFisheryBiologyWhaleBody mass indexZoologyEcologyEndocrinology

Abstract

fetched live from OpenAlex

Abstract Conservation efforts of the beluga whale ( Delphinapterus leucas ) of the St. Lawrence estuary include a mortality surveillance program which has the objective of documenting the causes of mortality. The evaluation of the animal's body condition is a key component in the diagnostic process. There is currently no consensual method to measure or calculate body condition indices in beluga whales. Morphological measurements recorded during necropsy were used to design a scaled mass body condition index that was compared to currently used visual evaluation, and to alternative morphological indices. Beluga whales were separated into two size‐based groups. The scaled mass index was well correlated with analog‐visual‐scale derived scores in beluga whale &gt;290 cm, but not in animals &lt;290 cm. Both methods showed almost perfect agreement regarding the categorization of carcasses belonging to the first quartiles. The alternative indices that were best correlated with the scaled mass index were those calculated using the sacral circumference and the ventral adipose thickness in animals &lt;290 cm and the epaxial muscle mass and maximum circumference in beluga whales &gt;290 cm. These scaled indices could provide objective tools to evaluate body condition of stranded beluga whales.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.254
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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

Citations8
Published2021
Admission routes2
Has abstractyes

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