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Record W2548706081 · doi:10.1139/cjz-2016-0178

Spatial distribution of narwhal (<i>Monodon monoceros</i>) diving for Canadian populations helps identify important seasonal foraging areas

2016· article· en· W2548706081 on OpenAlexafffundvenueabout
Cortney A. Watt, Jack Orr, Steven H. Ferguson

Bibliographic record

VenueCanadian Journal of Zoology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of ManitobaFisheries and Oceans Canada
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaNunavut Wildlife Management BoardArcticNetGarfield Weston FoundationWorld Wildlife Fund
KeywordsForagingBayFisheryArcticBenthic zoneBiologyPredationPopulationGeographyEcologyOceanographyArchaeologyGeology

Abstract

fetched live from OpenAlex

In Canada, narwhals (Monodon monoceros L., 1758) are divided into the Baffin Bay (BB) and northern Hudson Bay (NHB) populations. Satellite tracking of 21 narwhals from BB and NHB provided information on their diving behaviour and was used to identify foraging regions. Previous research from hunted narwhals indicated that narwhals in both populations depend on benthic prey to meet their dietary needs. To evaluate home ranges and define areas important for benthic foraging, we conducted kernel density analysis on narwhal locations and focused on areas where deep diving occurs, as a proxy for foraging, in the winter, spring, and migratory periods. These analyses revealed important areas for foraging for BB narwhals on the summer grounds in Eclipse Sound, and the winter grounds in Davis Strait, as well as on the migratory pathway between regions. Similarly, important areas were identified for the NHB narwhal population in northwestern Hudson Bay in summer, in NHB and Hudson Strait on the migration, and to the east of the entrance to Hudson Strait in the winter. This, along with an analysis of the absolute dive depths, provides information on seasons and regions important for foraging, which is particularly relevant with increasing industrial activities in the Arctic.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.247
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2016
Admission routes4
Has abstractyes

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