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Record W2270959961

An ASPIC Based Assessment of Redfish (S. mentella and S. fasciatus) in NAFO Divisions 3LN (assuming that the highest apparently sustained historical average level of catch is a sound proxy to MSY)

2014· article· en· W2270959961 on OpenAlexaboutno aff
A. Ávila-de-Melo, Nuno M. Brites, R. Alpoim, D.M. González-Troncoso

Bibliographic record

VenueDIGITAL.CSIC (Spanish National Research Council (CSIC)) · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersEuropean CommissionDartmouth College
KeywordsSebastesFisheryStock assessmentCatch per unit effortGeographyFishingBiologyFish <Actinopterygii>
DOInot available

Abstract

fetched live from OpenAlex

There are two species of redfish in Divisions 3L and 3N, the deep-sea redfish (Sebastes mentella) and the Acadian redfish (Sebastes fasciatus) that have been commercially fished and reported collectively as redfish in fishery statistics. Redfish in Div. 3LN is regarded as a management unit composed of two Grand Bank populations from those two very similar redfish species. The present ASPIC assessment is based on the logistic form of a non-equilibrium surplus production model (Schaeffer, 1954; Prager, 1994), adjusted to a standardized catch rate series (Power, 1997) and to most of the stratified-random bottom trawl surveys conducted in various years and seasons in Div. 3L and Div. 3N from 1978 onwards. Both CPUE and surveys were used with all observations of each series.&#13;\nThis assessment is not a follow up of the previous ones (Ávila de Melo et al., 2012 and 2010). The logistic Schaefer production model (1954) incorporated in ASPIC operating model (Prager, 1994) can not cope anymore with the most recent biomass increases observed in both spring and (mainly) autumn Canadian 3LN surveys, unless it is allowed to provide unrealistic assessment results. And continuing to strip off the highs of each one of these series, in order to get a picture in line to what is the perception of the stock history from commercial and survey data trends, is no longer a valid option, as reflected on the last STACFIS research recommendation on this matter (NAFO, 2012).&#13;\nBeing so, input has been reframed opening room to a new combination of Canadian autumn 3L and 3N surveys. The inclusion of the Spanish spring survey on Div. 3N and the removal of the historical CPUE series have also been considered. Two selected frameworks options have finally run with MSY kept constant at an initial starting guess, instead of being estimated by the model. Before entering the latest (2013) ASPIC Suite flow, the input selected from exploratory analysis was submitted to a sensitivity test in order to evaluate the robustness of the new framework against variability on random number seed, start user guesses for key model parameters and last year survey biomass.&#13;\nThe consistency of the new ASPIC assessment with their predecessors was checked by comparison of biomass and fishing mortality fit trajectories against previous ones from the 2012 and 2010 assessments. A 2014-2012 retrospective analysis was also performed with good results (small retro bias on relative biomass and fishing&#13;\n2&#13;\nmortality in response to the general increase of the still standing survey series), and the assessment pursued successfully to bootstrap mode (again good consistency with previous results) and projections.&#13;\nA medium term management plan is finally proposed, based on bi-annual increases of the catch from the present TAC level of 6 500 t up to target catch/TAC of 18 100 t, the 2014 equilibrium yield from the present assessment, that should be in place by 2019-2020. This management plan allows, with a very high probability, that biomass is kept above Bmsy and fishing mortality below Fmsy.

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)0.009
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation 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 score0.472
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.007
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 science0.0010.001
Research integrity0.0000.001
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 head0.204
GPT teacher head0.364
Teacher spread0.161 · 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 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
Published2014
Admission routes1
Has abstractyes

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