Optimal treatment regimes from productivity and economic standpoints: The management of suspended mussel lines using high pressure water treatments for the vase tunicate, Ciona intestinalis
Bibliographic record
Abstract
The invasive tunicate Ciona intestinalis (L., 1767) had an economic impact on the aquaculture of the blue mussel Mytilus edulis (L., 1758) in Prince Edward Island (PEI) and other areas. This tunicate fouls mussel socks suspended on long lines in the water, increasing the weight of the lines and reducing the weight and number of mussels, decreasing overall productivity and profitability. This study determined the relative effects of high pressure water treatment schedules over a 4 month period on two representative sites located in Murray and Brudenell Rivers on PEI. Results indicated that initiating treatment early in the season (July) and treating another 2 or 3 times on a monthly basis had the greatest effect on reducing tunicate numbers and size and enabling greater mussel productivity and farm profitability. While the most effective treatment may ultimately be site-specific, the two sites in this study support the notion that beginning treatment when tunicates are small is one of the most significant parts of a treatment strategy. The optimal treatment strategy needs to be balanced with the most cost effective regime to maintain or improve the economic potential of a mussel farm. Results show that a treatment regime that includes three or four treatments consistently results in an economic advantage over treating two times. If treating mussel socks only two times, treating them early also shows substantial economic advantage.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".