Development of a Monitoring Framework for the Potential Establishment of a Commercial Whelk Fishery in the Maritimes Region (4VS, 4W)
Bibliographic record
Abstract
Whelk fishing has a long history throughout the range of the species. In the Fisheries and Oceans Canada (DFO) Maritimes Region, an offshore exploratory whelk fishery commenced within North Atlantic Fisheries Organization (NAFO) divisions 4W and 4Vs during 2012. To date, license holders have found several areas that have yielded high landings of whelk. In 4Vs, landings have been strong with continual growth as larger areas are explored and the Total Allowable Catch (TAC) increased. Landings have reached as high as 665 tonnes in 2018, with an average Catch Per Unit Effort (CPUE) from 2009 to 2019 of 14.9 kg/trap. In 4W, a single area yielding high landings of whelk has been identified recently with landings as high as 211 tonnes. The CPUE in this division is lower than that of 4Vs, with a mean CPUE from 2012 to 2019 of 3.5 kg/trap. Fisheries Management has requested advice from DFO Science to assess current metrics gathered by the license holders, as well as establish priority areas for research and analysis that will enable development of a stock assessment framework for offshore whelk. Developing an assessment of stock status is currently hampered by limited information with regards to natural abundance of whelk within fished areas and the spatial extent and variation of whelk populations. Currently, there are no independent surveys that adequately sample whelk. Information on whelk is based solely on data collected by the exploratory license holders, who are currently collecting a host of useful biological data. Metrics such as age- and size-at maturity could be refined through alteration of the methods used and through defining the timing of the reproductive cycles. Most importantly, though, is the need to identify population structure. This species exhibits a low dispersal potential due to its direct development in benthic egg capsules and low adult movement. This results in local adaptation and potential genetic differences at small spatial scales. Whelk are vulnerable to local depletion due to these factors and their management should be applied to biologically relevant management units such as subpopulations. Management can be informed by many of the metrics currently collected by industry, and identifying population structure has been prioritized in the research plans of the license holders. Further work to identify the spatial extent of various subpopulations should be prioritized to establish these management units and to determine appropriate management strategies (such as Minimum Legal Size [MLS] informed by the life-history traits of the respective subpopulations).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".