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Prey biomass dynamics in gray whale feeding areas adjacent to northeastern Sakhalin (the Sea of Okhotsk), Russia, 2001–2015

2019· article· en· W2917290548 on OpenAlexaff
Arny L. Blanchard, Natalia L. Demchenko, Lise A. M. Aerts, Sergei B. Yazvenko, В. В. Ивин, Ilya A. Shcherbakov, H. R. Melton

Bibliographic record

VenueMarine Environmental Research · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsLGL (Canada)
Fundersnot available
KeywordsOceanographyPredationBiomass (ecology)Environmental scienceMarine ecosystemPopulationEcologyHabitatEcosystemDetritivoreFisheryGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Changing climate patterns strongly influence marine ecosystems across the Pacific Arctic region creating significant ecosystem transitions and change. Macrobenthic species are essential prey for numerous marine mammals and seabirds but the influence of climatic drivers that control macrobenthic community population dynamics are poorly known in critical prey habitats. We investigated associations of environmental, temporal, and climatic covariates with the biomass concentrations of six prey groups (Actinopterygii, Amphipoda, Bivalvia, Cumacea, Isopoda, and Polychaeta) in essential habitats for Korean-Okhotsk (western) gray whales adjacent to northeastern Sakhalin Island in the Sea of Okhotsk. Prey community biomass concentrations were correlated with water depth, year, and climate indices reflecting oceanographic and climatic patterns associated with macro-scale climatological effects. The correlation of prey biomass with water depth and year accounted for ∼90% of total variation in canonical correlation analyses (CCor). Climate indices accounted for ∼10% of total variation in CCor. Water circulation in winter may be particularly important for maintaining populations through the advection of particulate organic carbon entrained in winter currents. Overall, temporal trends in the biomass concentrations of gray whale prey resources appear to reflect climatic and oceanographic factors that are driving ecosystem changes across the Sea of Okhotsk and the Pacific Arctic region.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.257
Teacher spread0.240 · 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; both teacher heads agree on what is shown here.

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

Citations25
Published2019
Admission routes1
Has abstractyes

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