MétaCan
Menu
Back to cohort
Record W2510072232 · doi:10.1111/1365-2435.12729

Accelerometers can measure total and activity‐specific energy expenditures in free‐ranging marine mammals only if linked to time‐activity budgets

2016· article· en· W2510072232 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFunctional Ecology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNorth Pacific Research BoardNational Oceanic and Atmospheric Administration
KeywordsForagingEnergy expenditureAccelerometerBiologyDoubly labeled waterRangingAccelerationEcologyPredationEnergeticsEnergy metabolismGeodesyGeographyPhysics

Abstract

fetched live from OpenAlex

Summary Energy expenditure is an important component of foraging ecology, but is extremely difficult to estimate in free‐ranging animals and depends on how animals partition their time between different activities during foraging. Acceleration data have emerged as a new way to determine energy expenditure at a fine scale but this needs to be tested and validated in wild animals. This study investigated whether vectorial dynamic body acceleration (Ve DBA ) could accurately predict the energy expended by marine predators during a full foraging trip. We also aimed to determine whether the accuracy of predictions of energy expenditure derived from acceleration increased when partitioned by different types of at‐sea activities (i.e. diving, transiting, resting and surface activities). To do so, we equipped 20 lactating northern ( Callorhinus ursinus) and 20 lactating Antarctic fur seals ( Arctocephalus gazella ) with GPS , time‐depth recorders and tri‐axial accelerometers and obtained estimates of field metabolic rates using the doubly labelled water ( DLW ) method. Ve DBA was derived from tri‐axial acceleration, and at‐sea activities (diving, transiting, resting and surface activities) were determined using dive depth, tri‐axial acceleration and travelling speed. We found that Ve DBA did not accurately predict the total energy expended by fur seals during their full foraging trips ( R 2 = 0·36). However, the accuracy of Ve DBA as a predictor of total energy expenditure increased significantly when foraging trips were partitioned by activity and when activity‐specific Ve DBA was paired with time‐activity budgets ( R 2 = 0·70). Activity‐specific Ve DBA also accurately predicted the energy expenditures of each activity independent of each other ( R 2 > 0·85). Our study confirms that acceleration is a promising way to estimate energy expenditures of free‐ranging marine mammals at a fine scale never attained before. However, it shows that it needs to be based on the time‐activity budgets that make up foraging trips rather than being derived as a single measure of Ve DBA applied to entire foraging trips. Our activity‐based method provides a cost‐effective means to accurately calculate energy expenditures of fur seals using acceleration and time‐activity budgets, that can be transfered to studies on other species.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.989

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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 head0.015
GPT teacher head0.203
Teacher spread0.189 · 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