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Record W6892073294 · doi:10.5061/dryad.2jm63xsqh

Data from: Environmental drivers of population-level variation in the migratory and diving ontogeny of an Arctic top predator

2022· dataset· en· W6892073294 on OpenAlexaff

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsFisheries and Oceans Canada
FundersNatural Environment Research Council
KeywordsArcticForagingOntogenyApex predatorPredationSea icePredatorForage

Abstract

fetched live from OpenAlex

The development of migratory strategies that enable juveniles to survive to recruitment is critical for species that exploit seasonal niches. For animals that forage via breath-hold diving this requires a combination of both physiological and foraging skill development. Here, we assess how migratory and dive behaviour develop over the first months of life for a migratory Arctic top predator, the harp seal, tracked using animal-borne satellite relay data loggers. We reveal similarities in migratory movements and differences in diving behaviour between juveniles from breeding populations in the Northwest Atlantic and Greenland Sea. In both regions, periods of resident and transient behaviour during migration were associated with proxies for food availability; sea ice concentration and water depth. However, while ontogenetic development of dive behaviour was similar for both groups of juveniles over the first 25 days, after this time Greenland Sea animals performed shorter and shallower dives and were more closely associated with sea ice than Northwest Atlantic animals. Together, these results highlight the role of both intrinsic and extrinsic factors in shaping early-life behaviour. Differences in the environmental conditions experienced during early-life may shape how populations respond to the rapid changes occurring in the Arctic ocean ecosystem.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.738
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.3190.000

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 head0.093
GPT teacher head0.289
Teacher spread0.196 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2022
Admission routes1
Has abstractyes

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