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Record W2091512435 · doi:10.1139/f04-054

Movements of lingcod (<i>Ophiodon elongatus</i>) in southeast Alaska: potential for increased conservation and yield from marine reserves

2004· article· en· W2091512435 on OpenAlexvenueno aff
Richard M. Starr, Victoria O’Connell, Stephen Ralston

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersNational Marine Fisheries ServiceCalifornia Sea Grant, University of California, San DiegoDavid and Lucile Packard Foundation
KeywordsMarine reserveFishingFisheryBiologyStock (firearms)Fish stockGeography

Abstract

fetched live from OpenAlex

Residence time and movement rates of lingcod (Ophiodon elongatus) were recorded in an area closed to fishing in southeast Alaska to evaluate the potential effects of reserves on mortality, egg production, and fishery yield. In 1999, 43 lingcod were tagged with sonic transmitters, and an array of receivers moored in the reserve recorded signals transmitted from tagged fish for 14 months. Most of the tagged fish frequently left the reserve but were only absent for short time periods. Tagged fish showed a high degree of site fidelity. Models generated from the tag data provided a way to predict the effects of marine reserves on yield and eggs per recruit for a cohort of female lingcod. Model results indicated that for lingcod stocks with low abundance, marine reserves could improve egg production while having a small effect on fishery yield. For more abundant stocks, if a portion of the stock is protected in reserves, fishing rates could be increased outside reserves without reducing egg production relative to egg production levels in the absence of reserves.

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 score0.950
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

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 communication0.0000.000
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 head0.021
GPT teacher head0.223
Teacher spread0.202 · 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

Citations52
Published2004
Admission routes1
Has abstractyes

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