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Record W7133284611

Application of the management procedure framework for Inside Quillback Rockfish (Sebastes maliger) in British Columbia in 2021

2024· other· en· W7133284611 on OpenAlexaboutno aff
Quang C. Huynh, Matthew R. Siegle, Dana Haggarty

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsStock (firearms)Operating modelPopulationStock assessmentSustainable managementRockfishFisheries management
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this project is to provide scientifc advice to support management of Inside Quillback Rockfsh (Sebastes maliger). The stock is expected to be prescribed as a major fsh stock, at which time its sustainable management will be legislated under the Fish Stocks Provisions of the Fisheries Act. This analysis applied the Management Procedure (MP) Framework, recently developed for British Columbia (BC) groundfshes, to evaluate the performance of index-based and constant catch MPs, with respect to meeting policy and fshery objectives. To account for uncertainty in underlying population dynamics and data sources, we developed fve alternative operating model (OM) scenarios, which differed with respect to specifc model and data assumptions. Operating models were conditioned on historical catches, indices of abundance, and age composition. Three reference OMs varied on the assumption of the natural mortality value for Inside Quillback Rockfsh. Two additional robustness OMs were developed, with one developed by excluding a historical jig survey in Area 12, and another that modeled lower than average recruitment in the projection. The reference OMs indicated the stock was above the LRP (0.4 BMSY) with at least 50% probability in 2021. The index from the jig survey is impactful on the historical stock trajectory, but is indicative of the declining stock trend that led to the rockfsh conservation strategy in the early 2000s. Two fxed catch MPs of 33 tonnes (the average catch during 2012-2019) and 41 tonnes (125% of the 2012-2019 mean) and eight index-based MPs (Iratio, GB_slope, and IDX with various tuning parameters) that adjust the catch based on the recent trend in the index of abundance from the inside hard-bottom longline (HBLL) survey were tested in the closed-loop simulations. In the reference set, all MPs passed the proposed satisfcing criterion with the stock exceeding the LRP with at least 75% probability after one generation (24 years). The satisfcing criterion was also met in both robustness operating models. Visualizations present trade-offs in tabular and graphical formats to support the process of selecting the fnal MP. There is a trade-off between biomass and fshery catches after one generation with higher catches with Iratio management procedures compared to the others. Tradeoffs in short-term and long-term catch were evident in the short-term (7 years) and after one generation. The tradeoff was less evident over longer time scales (after one vs. three generations or after 24 vs. 72 years). MPs that advise high catches after one generation continue to do so after three generations. We propose operating models to be identifed in the reference set when used to identify stock status. We also provide future research recommendations regarding commercial fshery biological sampling and Food, Social, and Ceremonial (FSC) catch. We make recommendations to use the HBLL index of abundance and HBLL mean weight to identify triggers for future re-assessment.

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.351

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.229
Teacher spread0.223 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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Same venueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada→French-language works237,207→