Assessing the Technical and Economic Viability of Galvanizing Snow Crab (Chionoecetes opilio) Traps
Bibliographic record
Abstract
Commercial harvesting of snow crabs (Chionoecetes opilio) began in Newfoundland and Labrador, Canada, in 1967. Today, the fishery consists of 2188 active fishing licenses and has grown into the province’s most economically valuable fishery. Snow crabs are captured using conical traps consisting of a mild carbon steel frame, hard plastic entry funnel and a jacket of polyethylene netting. The frames of these traps corrode over time, which is expedited by being deployed in marine environments and stored on land near the ocean when not in use. As a result, there is interest within the community to increase the longevity of crab traps. One solution is to galvanize the steel frames prior to installing the funnel and netting. However, before harvesters transition to galvanized traps, two questions must be answered. Will the use of galvanized steel negatively impact catch rates? Will the life cycle of a crab trap be extended sufficiently to justify the additional cost of galvanizing? This study employed a generalized linear mixed model to evaluate the catch of legal-sized male crabs (CPUE) during the commercial fishery as a function of three trap frame treatments (old traditional, new traditional and new galvanized). We also assessed the economic viability of galvanizing trap frames by evaluating the life cycle cost (LCC) of traditional and galvanized traps to the harvester. The LCC was calculated over a range of inflation (0–6%) and discount (3–20%) rates. Our results found no significant difference in CPUE between new traps (traditional vs. galvanized) and concluded that except during instances of very high discount rates (12.9–19.9%), it is economically favourable to galvanize crab trap frames.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".