Differentiating fish targets from non-fish targets using an imaging sonar and a conventional sonar: Dual frequency identification sonar (DIDSON) versus split-beam sonar
Bibliographic record
Abstract
A key requirement in applying acoustic techniques to estimating fish abundance is the removal of non-fish targets from the database. In a riverine environment, debris, entrained air bubbles, bottom objects are common ambient targets which can effectively scatter the probing sound from a fisheries sonar system, and cause strong echoes for the system. A conventional sonar system provides limited and highly simplified information for a detected target, which results in difficulty in separating fish from other targets. Recently developed DIDSON sonar utilizes imaging sonar technology to provide photo-quality images of underwater objects, making possible the visual interpretation of targets. A DIDSON system was deployed in the Fraser River at Mission, British Columbia during the salmon migration in 2004. Data were collected simultaneously from the DIDSON sonar and from a 200-kHz split-beam sonar. These data allow for comparisons of estimates of upstream salmon flux acquired concurrently by the imaging and the split-beam sonar systems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".