Bibliographic record
Abstract
Les méthodes hydroacoustiques constituent un outil important pour repérer et évaluer les poissons et le plancton. Divers appareils acoustiques puissants sont maintenant utilisés couramment par les pêcheurs, les gestionnaires et les scientifiques. Leur efficacité repose sur plusieurs principes fondamentaux qui rendent possible la télédétection de paramètres biologiques sous l'eau. Un certain nombre de techniques acoustiques sont utilisées régulièrement à la Station de biologie du Pacifique située à Nanaimo, et on en donne ici une brève description. On utilise, à titre d'exemples, un levé par intégration des échos de merlus du Pacifique et une expérience qui compare un dénombrement visuel et acoustique de saumons. Hydroacoustic methods provide an important tool to detect and assess fishes and plankton. A variety of powerful acoustic devices are now in general use by fishermen, managers and scientists. Their success is based on several fundamental principles which make underwater remote sensing of biological parameters possible. A number of acoustic techniques, routinely used at the Pacific Biological Station in Nanaimo are briefly described. An echo integration survey of Pacific hake and an experiment that compares a visual and an acoustic count of salmon are used as illustrations. Hydroacoustic methods play an important role in commercial fishing, fisheries management and fisheries research. The three endeavours rely on similar methods, but differ in the required accuracy, precision and timeliness of the results. The most common acoustic device in fisheries is the vertically oriented sonar (echosounder), Fig 1. The usefulness of the echosounder and other hydroacoustic devices in fisheries is based on several phenomena:
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.453 | 0.274 |
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".