Assessment of the Counting Accuracy of the Vaki Infrared Counter on Chum Salmon
Bibliographic record
Abstract
Abstract Vaki, Ltd., of Iceland has designed a system for counting the in-river migration of salmonids via infrared sensors. The Vaki fish counter is used in Iceland, the United Kingdom, and Europe but is much less used in North America partly because of the system's unknown ability to count large populations accurately. In tests in the Big Qualicum River of Vancouver Island, British Columbia, we found the accuracy of the counter to be inversely correlated with migration rate of chum salmon Oncorhynchus keta. The fish counter was very accurate (>95%) for migration rates less than 500 fish/h but accuracy declined to 76% at a rates exceeding 1,500 fish/h. The principal cause for the decline in accuracy was the inability of the infrared sensors to count the passage of more than one fish simultaneously.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".