Implementing sustainable harvesting of Arctic Surf Clam in Atlantic Canada through the use of high-resolution seafloor habitat maps
Bibliographic record
Abstract
Clearwater Seafoods, founded in Nova Scotia in 1976, has grown into one of the world's leading seafood companies. The company's core philosophy is based on sustainability and stewardship at sea and onshore, recognizing that healthy oceans are fundamental to business success. The development and implementation of enhanced seafloor maps of fishing grounds, depicting predicted habitat distributions of target species, is one of Clearwater's primary objectives within their Harvest Science operations. These onboard visualizations contribute to the development of corporate resource-management frameworks for (1) sustainable stock assessment for the spatial regions, (2) improved offshore harvest efficiencies for the target species, and (3) significant reductions of the fishery's footprint on the ocean floor. To contribute towards achieving these objectives, Clearwater Harvest Science has been collaborating with the Nova Scotia Community College to develop seafloor habitat maps for its clam fishery using multibeam echo sounder (MBES) data collected in 2007 and 2008 on Banquereau Bank, and more recently in 2016 on the Grand Banks of Newfoundland. Here we demonstrate how MBES backscatter, bathymetry, and secondary-derived bathymetric layers (e.g. slope, curvature, etc.) have been used to model predictive habitat for Arctic Surf Clam (Mactromeris polynyma) - Clearwater's target fishery on the banks. Utilizing onboard visualization applications, Clearwater's Fishing Masters are able to target, with precise accuracy, the most preferred benthic areas of the banks. Species Distribution Modelling (SDM) maps and images are being developed and continuously evaluated along with commercial catch data from the Clearwater Harvest Management database to demonstrate the credibility and validity of the maps and to measure the company's harvest efficiencies and subsequent performance measurement. SDM maps, along with backscatter and bathymetry information, have become important tools and are routinely used aboard Clearwater's clam fishing vessels to target fishing areas thus providing for sustainability of the ocean and resource, and the most efficient harvesting of quotas. Although only in the early stages of the project, evidence from fishing performance over recent months have suggested that the use of this information can increase catch rates, reduce bottom contact of the fishing gear, and results in a significant increase in revenue from a fishing trip. For a quota-based fishery, this demonstrates the combined economic and environmental benefits these advanced seafloor habitat maps can offer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".