MétaCan
Menu
← Back to cohort
Record W7133282279

Canary rockfish stock assessment 2022

2023· other· en· W7133282279 on OpenAlexfundaboutno aff
Fisheries and Oceans Canada, Pêches et Océans Canada

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersFisheries and Oceans Canada
KeywordsStock assessmentStock (firearms)RockfishCatch per unit effortPopulationAerial survey
DOInot available

Abstract

fetched live from OpenAlex

The Canary Rockfish (CAR) stock assessment evaluates a British Columbia (BC) coastwide population harvested by two fisheries, one using combined bottom and midwater trawl gear and the other using non-trawl gear. Bottom trawl catches are predominant (83% by weight over the period 1996 to 2021), followed by midwater trawl (13%) and hook and line (4%). Analyses of biology and distribution did not support separate regional stocks for CAR. The CAR stock was assessed using an annual two-sex catch-at-age model, implemented in a Bayesian framework to quantify uncertainty of estimated and derived parameters. The analysis platform adopted was the National Oceanic and Atmospheric Administration’s (NOAA) Stock Synthesis 3. A base run that estimated natural mortality (M) and steepness (h) fit the available data credibly and was considered sufficient to model the population. This stock assessment was primarily informed by six CAR abundance series from fishery independent surveys, and a catch per unit effort (CPUE) abundance series. While the CAR survey abundance series had large relative errors, they did not contradict the commercial CPUE index series. Additionally, age frequency data from the commercial trawl fishery (36 years) and three survey series (23 years) were used. The median (with 5th and 95th percentiles) female spawning biomass at the beginning of 2023 (B2023) was estimated to be 0.78 (0.57, 1.05) of the equilibrium unfished female spawning biomass (B0). Also, B2023 was estimated to be 3.04 (1.92, 4.89) times the equilibrium female spawning biomass at maximum sustainable yield, BMSY. There was an estimated probability of 1 that B2023 > 0.4BMSY and a probability of 1 that B2023 > 0.8BMSY (i.e., of being in the Healthy zone). The probability that the exploitation rate in 2022 was below that associated with MSY was 1 for the combined commercial fisheries. Advice to managers was presented in the form of decision tables using the suggested reference points from Fisheries and Oceans Canada’s (DFO) Decision Making Framework Incorporating the Precautionary Approach (PA) (DFO 2009a). The decision tables provided ten-year projections across a range of constant catches up to 2000 tonnes/year. The recent five year (2017–2021) average catch was 789 t. The CAR stock was projected to remain above the limit reference point (LRP, 0.4BMSY) and upper stock reference (USR, 0.8BMSY) with a probability of >0.99 over the next 10 years at catch levels ≤1500 t/y. Catches ≤1250 t/y were predicted to keep the harvest rate below the harvest rate limit (uMSY) in 10 years with probability >95%. Reference points for long-lived, low productivity species are uncertain. Advice relative to MSY reference points was deemed appropriate for CAR; however, other B0 benchmark metrics were presented in the assessment document. It is recommended that a full re-assessment occurs in no more than 10 years, subject to the availability of new information. During intervening years, the trend in abundance can be tracked by commercial fishery CPUE and, less reliably (because of the high relative error), by the fishery independent surveys used in this stock 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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.700

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.011
GPT teacher head0.259
Teacher spread0.248 · 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 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
Published2023
Admission routes2
Has abstractyes

Explore more

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