MétaCan
Menu
Back to cohort
Record W2254055445 · doi:10.2495/eco030202

Precautionary Approach In Development Of A Directed Horse Clam FisheryIn British Columbia, Canada

2003· article· en· W2254055445 on OpenAlexaboutno aff
Z. Zhang

Bibliographic record

VenueWIT Transactions on Ecology and the Environment · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsFisheryFisheries managementPopulationReproductionGeographyEnvironmental scienceEcologyFishingBiologyDemography

Abstract

fetched live from OpenAlex

A directed fishery on horse clams is being developed in British Columbia, Canada. The paper describes how the development follows the guidelines of a framework, which explicitly endorses the precautionary approach. At the first phase of the development, biological and fisheries information on horse clams, such as longevity, reproductive characteristics, growth, mortality, and management schemes, was synthesised based on scientific literature, technical reports, and surveys. Growth parameters and natural mortality rates were estimated in the literature with certain degrees of uncertainties. At the second phase of the development, alternative management strategies were evaluated and a precautionary approach for setting up biological reference points was provided through a simulation study. A stochastic spawning stock biomass per recruit model was found to be able to fully utilise the available information. Uncertainties about the estimates of population parameters were incorporated in the simulation to produce a probability distribution of possible consequences in terms of reproduction potentials. After setting up a limit reference point, the fisheries managers are able to find from the probability distribution such an exploitation rate that the risk of reducing the reproduction potential below the limit reference point is small (e.g. 5%).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

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)0.0090.000

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 head0.006
GPT teacher head0.163
Teacher spread0.158 · 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.

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

Citations0
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueWIT Transactions on Ecology and the EnvironmentSame topicMarine and fisheries researchFrench-language works237,207