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Record W2112519860 · doi:10.14430/arctic790

Examination of Beluga-habitat Relationships through the Use of Telemetry and a Geographic Information System

2001· article· en· W2112519860 on OpenAlexafffundvenueabout
David G. Barber, Eric Saczuk, Pierre R. Richard

Bibliographic record

VenueARCTIC · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsFisheries and Oceans CanadaGovernment of CanadaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of CambridgeFisheries Joint Management CommitteePinngortitaleriffik
KeywordsBathymetryBeluga WhaleArcticHabitatLeucasOceanographyGeographyPhysical geographyBelugaSea iceFisheryEnvironmental scienceGeologyEcologyBiology

Abstract

fetched live from OpenAlex

The spatial and temporal relationships between belugas (Delphinapterus leucas) and two characteristics of their habitat - bathymetry and ice concentration - were examined. Observed location-habitat correspondence histograms were compared to random location-habitat histograms, using a Kolmogorov-Smirnoff (K-S) statistical test. Results show that beluga distribution is bimodal with respect to bathymetry, with a larger mode in shallow water and a smaller mode in water approximately 500 m deep. They occur more often than expected by chance in the 0/10 ice class and less often than expected in the 10/10 ice class. Males and females associate differently with both depth and ice concentration. Females associate with bathymetry very differently in the fall than in the summer. There is a general tendency for males in the eastern North American Arctic to be associated with shallow water during the summer and deeper water (modes at 100 and 500 m) in the fall. Female locations are associated more often with the 0/10 ice class and less often with the 10/10 class than expected by chance. These trends were stronger in the western than in the eastern portions of the Canadian 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.477

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.197
Teacher spread0.166 · 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.

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

Citations38
Published2001
Admission routes4
Has abstractyes

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Same venueARCTICSame topicArctic and Antarctic ice dynamicsFrench-language works237,207