Variation in the performance of acoustic receivers and its implication for positioning algorithms in a riverine setting
Bibliographic record
Abstract
The performance of an array of data-logging single frequency acoustic receivers in the Caloosahatchee River (Florida, USA) was examined and the results incorporated into a positioning algorithm for animals tracked within the system. The mean code detection efficiency across all individual receivers and all download periods was 0.414 detections per synchronization code. On average, the code rejection coefficient was approximately 4%, indicating that it was only a minor factor in reducing code detection efficiency. There were significant performance differences between stations and download periods, but no interaction between these two factors for all three metrics. Code detection efficiency, the rejection coefficient, and the noise quotient all showed significant variations with distance from the river mouth and time since deployment. Comparison of position estimates with and without efficiency produced small differences for bull sharks (Carcharhinus leucas) and cownose rays (Rhinoptera bonasus) monitored via this system. Root mean square errors were higher for cownose rays (48 m) than for bull sharks (23 m). Mean differences for individuals were always slightly downstream because of the increasing code detection efficiency of upriver receivers. The results of this comparison indicated that the inclusion of code detection efficiency did not significantly improve the results of the positioning algorithm.
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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 | 0.004 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".