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Record W2137970374 · doi:10.1139/f04-136

Precaution in the harvest of Methuselah's clams the difficulty of getting timely feedback from slow-paced dynamics

2004· article· en· W2137970374 on OpenAlexvenueno aff
JM Orensanz, Claudia M. Hand, Ana M. Parma, Juan L. Valero, Ray Hilborn

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityProductivityPopulationMaximum sustainable yieldFisheries managementFisheryTerm (time)Environmental scienceComputer scienceEnvironmental resource managementEcologyEconomicsBiologyDemography

Abstract

fetched live from OpenAlex

Geoduck (Panopea abrupta) stocks are perceived as stable and their fisheries as sustainable, but this may reflect a mismatch between slow-paced dynamics (maximum recorded age 168 years) and short-term perception. Management is based on biological reference points, whose appropriateness as a means to ensure sustainability is limited by a sedentary lifestyle and long-term trends in productivity. Analysis of age frequency distributions for 1979–1983, postharvest recovery rates measured in Washington in tracts pulse-fished during the 1980s and 1990s, and age frequency distributions compiled in British Columbia during the 1990s consistently suggest that recruitment declined for decades (long before the onset of the fishery), reaching a minimum around 1975, and rebounded afterwards. In such scenario, reliance on biological-reference-point-based harvest rules without timely feedback could accelerate population declines, eventually driving an apparently sustainable fishery to collapse. The merits of approaches that rely on monitoring and feedback using data-driven decision rules are stressed. Transition from a biological-reference-point-based strategy to one based on monitoring and feedback will demand a shift in research focus to the design of practical monitoring programs and the evaluation of management procedures by means of simulations. For geoducks and other long-lived organisms, monitoring should integrate data informative at different temporal scales.

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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.227
Teacher spread0.209 · 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

Citations57
Published2004
Admission routes1
Has abstractyes

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