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Record W7095317716

Estimation of Stock Reproductive Potential: History and Challenges for Canadian Atlantic Gadoid Stock Assessments

2015· article· en· W7095317716 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEducational Robotics and Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsGroundfishStock (firearms)PopulationStock assessmentEstimationFisheries management
DOInot available

Abstract

fetched live from OpenAlex

The knowledge and scientific history of maternal and paternal aspects of reproduction dates back several decades, though only recently are fishery scientists and managers giving this subject area greater attention in the provision of management advice. This paper outlines and summarizes several recent advances made in understanding the importance of variation in gadoid reproductive output as a function of spawner age, size, maturation, condition and reproductive history. An assembled score-card applied to 45 Canadian Atlantic gadoid stock assessments conducted from 1985 to 1998 portrays the evolution and current degree to which this scientific knowledge has been incorporated into fishery advice in eastern Canada. For future research, recommendations are made which promote the integration of basic reproductive biology into groundfish stock assessments. These emerging policies encourage managers worldwide to initiate relevant sampling programs which will lead to improved stock conservation reference points. A new term referred to as stock reproductive potential (SRP) is introduced. Compared to spawning stock biomass and population fecundity, SRP more accurately represents the annual variation in a stock's ability to produce viable eggs and larvae that may eventually recruit to the adult population or fishery. SRP is a term (concept) which will likely evolve in such a manner as to provide a more accurate measure of a stock's reproductive potential.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score1.000

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)0.0000.000

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 head0.084
GPT teacher head0.287
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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