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Record W4307889031 · doi:10.1111/mms.12982

Estimating body mass of sperm whales from aerial photographs

2022· article· en· W4307889031 on OpenAlexaff
Maria Glarou, Shane Gero, Alexandros Frantzis, José María Brotons, Fabien Vivier, Paraskevi Alexiadou, Margalida Cerdà, Enrico Pirotta, Fredrik Christiansen

Bibliographic record

VenueMarine Mammal Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsCarleton University
FundersMinisterio para la Transición Ecológica y el Reto DemográficoOticon FondenUniversity of ManchesterMinistry of EnvironmentCarlsbergfondetVillum FondenNational Geographic Society
KeywordsCetaceaAerial surveyGeographySperm whaleBiologyFisheryEnvironmental scienceCartography

Abstract

fetched live from OpenAlex

Abstract Body mass is a fundamental feature of animal physiology. Although sperm whales ( Physeter macrocephalus ) are the largest toothed predators on earth, body mass is seldom included in studies of their ecophysiology and bioenergetics due to the inherent difficulties of obtaining direct measurements. We used UAV‐photogrammetry to estimate the weight of free‐ranging sperm whales. Aerial photographs (23 calves, 11 juveniles, 55 nonmother adults, 13 mothers) were collected in the Eastern Caribbean and Mediterranean Sea during 2017–2020. Body length, widths, and heights (dorso‐ventral distance at 5% increments) were measured from dorsal and lateral photographs, while body volume was calculated using an elliptical model. Volume varied noticeably (12.01 ± 4.79 m 3 ) in larger animals (>8 m), indicating fluctuations in body condition of adults and mothers. Volume was converted to mass, using tissue‐density estimates from catch data, animal‐borne tags, and body‐tissue composition. Average total body density ranged from 834 to 1,003 kg/m 3 , while the weight predictions matched with existing measurements and weight‐length relationships. Our body‐mass models can be used to study sperm whale bioenergetics, including inter‐ and intraseasonal variations in body condition, somatic growth, metabolic rates, and cost of reproduction.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.972

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 communication0.0000.000
Open science0.0010.007
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0290.000

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 head0.011
GPT teacher head0.229
Teacher spread0.217 · 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

Citations29
Published2022
Admission routes1
Has abstractyes

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