MétaCan
Menu
Back to cohort
Record W2158681969 · doi:10.1111/mms.12220

An apparent population decrease, or change in distribution, of Weddell seals along the Victoria Land coast

2015· article· en· W2158681969 on OpenAlexaff
David G. Ainley, Michelle LaRue, Ian Stirling, Sharon Stammerjohn, Donald B. Siniff

Bibliographic record

VenueMarine Mammal Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of AlbertaAlberta Environment and Protected Areas
FundersNational Aeronautics and Space AdministrationUniversity of CanterburyNational Science Foundation
KeywordsSea icePopulationBaySound (geography)OceanographyGeographyContext (archaeology)HabitatFisheryPhysical geographyEcologyGeologyBiologyArchaeology

Abstract

fetched live from OpenAlex

Abstract Ground counts during 1959–1968 compared with counts using high resolution (0.6 m 2 ) satellite imagery during 2008–2012 indicated many fewer Weddell seals ( Leptonychotes weddellii ) at two major molting areas in the western Ross Sea: Edisto Inlet‐Moubray Bay, northern Victoria Land, and McMurdo Sound, southern Victoria Land. Breeding seals have largely disappeared from Edisto‐Moubray, though the breeding population in McMurdo Sound appears to have recovered from harvest in the 1960s. The timing of decline, or perhaps spreading (lower numbers of seals in more places), is unknown but appears unrelated to changes in sea ice conditions. We analyzed both historic and satellite‐derived ice data confirming a large expansion of pack ice mostly offshore of the Ross Sea, and not over the continental shelf (main Weddell seal habitat), and a thinning of fast ice along Victoria Land (conceivably beneficial to seals). Timing of fast ice presence and extent in coves and bays along Victoria Land, remains the same. The reduction in numbers is consistent with an altered food web, the reasons for which are complex. In the context of a recent industrial fishery targeting a seal prey species, a large‐scale seal monitoring program is required to increase understanding of seal population changes.

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)0.001
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 score0.076
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.041
GPT teacher head0.287
Teacher spread0.246 · 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

Citations45
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueMarine Mammal ScienceSame topicMarine animal studies overviewFrench-language works237,207