Salmon enumeration in the Fraser River with the dual-frequency identification sonar (DIDSON) acoustic imaging system
Bibliographic record
Abstract
Reliable data on the number of salmon entering tributaries of the Fraser River to spawn (escapement) is needed for Pacific salmon management. Existing escapement techniques are costly and the number of populations requiring assessments has risen because of stock rebuilding efforts. The efficacy of a DIDSON acoustic imaging system for salmon stock assessment was investigated. Sixteen potential sites within the Fraser watershed were surveyed and based on channel morphology, bottom morphology, flow pattern, fish behavior and location relative to spawning grounds, ten sites in six rivers meet the needs of fisheries managers and the DIDSON system for escapement estimates. Fish count data from the DIDSON were compared to data from a counting fence (used as a standard) using regression techniques, resulting in relationships with slopes ranging from 0.98 to 1.02. The precison of DIDSON counts >50 (measured by CV) among three readers was 1.7%. This work supports the conclusion that the DIDSON system can deliver escapement estimates whose accuracy, precision and scientific defensibility is consistent with or better than existing escapment techniques and at a lower operating cost to assessment programs. [Work supported by the Southern Boundary Restoration and Enhancement Fund of the Pacific Salmon Commission.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".