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Record W2096534909 · doi:10.1139/f00-215

Allometry of natural mortality as a basis for assessing optimal release size in fish-stocking programmes

2000· article· en· W2096534909 on OpenAlexvenueno aff
Kai Lorenzen

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsAllometryStockingStatisticsMathematicsMortality rateGrowth rateExponentBiologyEcologyDemographyFishery

Abstract

fetched live from OpenAlex

This study evaluates the use of general mortality-size relationships for the assessment of release size in stocked fisheries. Seven release experiments (53 stocking events) are analysed, using a survival model based on allometric mortality and linear-length growth, allowing variation between experiments in both the allometric exponent and the level of mortality at reference length or generalising in one or both of the parameters. Results support the existence of a consistent allometry that applies independently of the overall level of mortality. The best-performing model is one in which the length exponent of mortality is set to -1 a priori, while mortality at reference length is allowed to vary between experiments (ranging from 0.7 to 33 per year at 15 cm in the present study). Even though the allometry of mortality is constant, the relative survival advantage of stocking large fish increases with the level of mortality at reference length. Using the identified length exponent of mortality of -1, survival models are derived for the linear, exponential, and von Bertalanffy growth equations. The models can be used to assess alternative release sizes, given an estimate of mortality at reference length, to facilitate comparative studies and to aid in the design of release experiments.

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)0.001
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 score0.224
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.287
Teacher spread0.261 · 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

Citations209
Published2000
Admission routes1
Has abstractyes

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