The critical detection distance for passively tracking tagged fish using a fixed radio telemetry station in a small stream
Bibliographic record
Abstract
Abstract Background Fixed radio telemetry stations are used to study the movement ecology of fishes in streams and rivers. A common assumption of such studies is that detection efficiency remains constant through space and time. The objective of this study was to understand how site characteristics and tag distance can influence the detection efficiency of a fixed receiver when used for fisheries research in a small stream. Field tests included a fixed receiver station on Forty Mile Creek, in Banff National Park, AB, Canada that recorded signals from radio tags over specified distances (i.e., 0 m, 27 m, 53 m, 80 m) within the expected detection range from July to October, 2016. Model selection was used to test which parameters may influence detection efficiency. Results The fixed receiver was able to record an average of 89% of transmissions over the study period. Detection efficiency was greater or equal to 0.97 at tag distances of ≤ 53 m. Detection efficiency significantly declined by 36% to a rate of 0.62 for tags placed 80 m from the fixed receiver. Water temperature and water depth also reduced detection efficiency, but only at the critical threshold of 80 m from the tag. Interestingly, turbidity had no influence on detection efficiency in this study. Conclusions This study provided insights into the reliability of fixed receiver stations as a passive tracking technique in small streams. The abrupt change in detection efficiency observed in this study presumably occurs in other systems. Identifying critical detection distance thresholds would appear to be a useful strategy for avoiding false-negative results. It is recommended that researchers who conduct radio tracking studies with fixed arrays should consider the deployment of sentinel tags over the study to understand the system performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".