Management Methods for a Sea Urchin Dive Fishery with Individual Fishing Zones
Bibliographic record
Abstract
Management of the Nova Scotia sea urchin fishery includes several unusual features: one license per fishing zone, fishers increase resource yields over natural levels by controlling the sea urchin-macrophyte cycle, fishers scale fishing effort to market demand, fishers map the resource in their zones, a reference point for good resource management based on a conspicuous habitat feature, an audit of zone management success, and low ongoing input from the management agency. The low mobility of sea urchins and the opportunity for the diver-harvesters to observe the resource directly make this fishery a good candidate for management by fishers. Variable sea urchin growth and reproduction on a small spatial scale and the high cost of stock surveys by diving make the fishery less suitable for government regulation. Fishing zones were allocated based on the length of feeding fronts (i.e., the deep edge of the macrophyte beds where sea urchins aggregate and where most harvesting occurs). Fishers and government jointly developed enhancement techniques to increase the length of feeding fronts. The reference point used to measure a fisher's success at managing the stock was based on the depth of these feeding fronts.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".