An estimation of the carrying capacity of a commercial mussel farm in Newfoundland
Bibliographic record
Abstract
The mussel industry in Newfoundland began in the early 1980’s. with the number of farms increasing rapidly over the next ten years. By the early 1990’s some of the farms had grown quite large, in excess of 100 hectares, and the industry was becoming concerned about the carrying capacity of some sites. -- This project was initiated to evaluate the carrying capacity of a commercial mussel farm, owned and operated by Atlantic Ocean Farms Ltd., in Fortune Harbour, Newfoundland. The site operators noted it was taking longer to obtain a market size mussel than it had in previous years. -- Over the two year study period, 1994-1996, mussels suspended at 2 m and 15 m and at opposite ends of the site were significantly different in shell length, dry tissue weight, dry shell weight and, in those near the surface, in condition. -- Chlorophyll-α, temperature, and salinity at 2 m were not significantly different at either location although both salinity and temperature at 2 m were significantly different than at 15 m. The site had a low current speed, <2 cm/s, low tidal flushing, and less than optimal chlorophyll-α concentrations with an annual mean of 1.6 µg/L. There were three different carrying capacity models used to determine an appropriate stocking density for the site: tidal volume method, food depletion approach, and food demand versus food supply. The stocking density present on the site, 65 x 10⁶ mussels in 1995, was more than two times the suggested stocking density based of these models. -- It is recommended the operators reduce density of mussels on the site and stock at a rate of approximately 14,000 socks annually or 35 x 10⁶ mussels (132 socks per hectare or 33 x 10⁴ mussels per hectare).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".