Bibliographic record
Abstract
An international survey revealed that eleven compounds representing five pesticide types are currently being used on commercial salmon farms for sea lice control. These include two organophosphates (dichlorvos and azamethiphos); three pyrethrin/pyrethroid compounds (pyrethrum, cypermethrin, deltamethrin); one oxidizing agent (hydrogen peroxide); three avermectins (ivermectin, emamectin and doramectin) and two benzoylphenyl ureas (teflubenzuron and diflubenzuron). The number of compounds available in any one country is highly variable, ranging from 9 (Norway) to 6 (Chile, United Kingdom) to 4 (Ireland, Faeroes, Canada) to 2 (US)). Dichlorvos, Azamethiphos and cypermethrin were the most widely used compounds (5 countries) followed by, hydrogen peroxide, ivermectin and emamectin (4 countries each), teflubenzuron (3 countries), diflubenzuron (2 countries), and deltamethrin, pyrethrum and doramectin (1 country each). Although, like trichlorfon, dichlorvos use is being discontinued in several countries notably Norway and the Faeroes. In most instances the availability of sea lice chemotherapeutants is limited, many being used under extra-label veterinary prescription or exemption, and special investigation permits. Access to a broad range of compounds with different modes of action, as well as application methods, has only recently been acquired making assessment of chemotherapy, and therefore integrated pest management, difficult.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".