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Record W2772836262 · doi:10.14430/arctic4682

Seasonal Movements and Relative Abundance of Bearded Seals (<i>Erignathus barbatus</i>) in the Coastal Waters of the Chukotka Peninsula

2017· article· en· W2772836262 on OpenAlexvenueno aff
Vladimir V. Melnikov

Bibliographic record

VenueARCTIC · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsPeninsulaSpring (device)OceanographyShoreArcticCapeGeographyFisheryGeologyArchaeologyBiology

Abstract

fetched live from OpenAlex

Information about bearded seal seasonal distribution in the Pacific Arctic is limited. Bearded seals (Erignathus barbatus Exleben, 1777) from coastal sites along the southern, eastern, and northern Chukotka Peninsula, Russian Federation, were observed most seasons during 1993 – 96, 1998 – 2000, 2002 – 05, and 2010 – 11. These observations provide spatial and temporal information about bearded seal seasonal distribution, movements, and relative numbers in the coastal zones. In winter, bearded seals aggregate on the young ice in the northern part of the Gulf of Anadyr. Numbers gradually increase during March. In springtime (April–May), bearded seals in the northern Gulf of Anadyr are relatively numerous around Nunligran (Cape Achen), but the number is highly variable across years. During spring bearded seals move eastward along the coast from the northern part of the Gulf of Anadyr towards the Bering Strait and then to the north, as the marginal ice edge zone retreats north. These movements to the east and north continue in ice-free water, and by August, the spring migration of bearded seals along the coast of the Chukotka Peninsula ends. In the summer months of August and September, few bearded seals are present in this coastal zone. The southward autumn migration of bearded seals is not evident near the coast, which suggests that it occurs farther from shore.

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 score0.022
Threshold uncertainty score0.355

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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