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Record W4391185311 · doi:10.47536/jcrm.v19i1.417

Patterns of predator-prey dynamics between gray whales (Eschrichtius robustus) and mysid species in Clayoquot Sound

2023· article· en· W4391185311 on OpenAlexaffabout
Robyn J. Burnham, D.A. Duffus

Bibliographic record

Venue˜The œjournal of cetacean research and management. Special issue · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPredatorPredationBiologyGray (unit)FisherySound (geography)EcologyGeographyOceanographyGeology

Abstract

fetched live from OpenAlex

The patterns of foraging intensity of gray whales (Eschrichtius robustus) over a 17-year period (1997–2013) in Clayoquot Sound, Vancouver Islandare examined. In this area, epibenthic mysid species are gray whales’ primary prey. The analysis indicates a top-down modification on habitatquality by this apex predator. Intense foraging in one or two summer season contributes to reduced prey resources available in the following summer.Years of heavy predation pressure were followed by at least one year of reduced foraging, probably allowing a reprieve in which the mysids couldrepopulate. Over the time span several patterns were noted including: boom-bust cycles; extended periods of reduced foraging; an overall decliningtrend of foraging whales using Clayoquot Sound, followed by a significant prey recovery in 2010. Life history patterns of mysids are discussed inthe context of their ability to recover from predation, and how this recovery during a reprieve may buffer the intensity of foraging from the previousyear. The continuing ability of mysids to recover from repeated and persistent removal will determine the use of Clayoquot Sound as a gray whaleforaging area in the future.

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 score0.870
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.047
GPT teacher head0.307
Teacher spread0.260 · 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

Citations15
Published2023
Admission routes2
Has abstractyes

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