Probability of acoustic transmitter detections by receiver lines in Lake Huron: results of multi-year field tests and simulations
Bibliographic record
Abstract
Advances in acoustic telemetry technology have led to an improved understanding of the spatial ecology of many freshwater and marine fish species. Understanding the performance of acoustic receivers is necessary to distinguish between tagged fish that may have been present but not detected and from those fish that were absent from the area. In this study, two stationary acoustic transmitters were deployed 250 m apart within each of four acoustic receiver lines each containing at least 10 receivers (i.e., eight acoustic transmitters) located in Saginaw Bay and central Lake Huron for nearly 2 years to determine whether the probability of detecting an acoustic transmission varied as a function of time (i.e., season), location, and distance between acoustic transmitter and receiver. Distances between acoustic transmitters and receivers ranged from 200 m to >10 km in each line. The daily observed probability of detecting an acoustic transmission was used in simulation models to estimate the probability of detecting a moving acoustic transmitter on a line of receivers. The probability of detecting an acoustic transmitter on a receiver 1000 m away differed by month for different receiver lines in Lake Huron and Saginaw Bay but was similar for paired acoustic transmitters deployed 250 m apart within the same line. Mean probability of detecting an acoustic transmitter at 1000 m calculated over the study period varied among acoustic transmitters 250 m apart within a line and differed among receiver lines in Lake Huron and Saginaw Bay. The simulated probability of detecting a moving acoustic transmitter on a receiver line was characterized by short periods of time with decreased detection. Although increased receiver spacing and higher fish movement rates decreased simulated detection probability, the location of the simulated receiver line in Lake Huron had the strongest effect on simulated detection probability. Performance of receiver lines in Lake Huron varied across a range of spatiotemporal scales and was inconsistent among receiver lines. Our simulations indicated that if 69 kHz acoustic transmitters operating at 158 dB in 10–30 m of freshwater were being used, then receivers should be placed 1000 m apart to ensure that all fish moving at 1 m s −1 or less will be detected 90% of days over a 2-year period. Whereas these results can be used as general guidelines for designing new studies, the irregular variation in acoustic transmitter detection probabilities we observed among receiver line locations in Lake Huron makes designing receiver lines in similar systems challenging and emphasizes the need to conduct post hoc analyses of acoustic transmitter detection probabilities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".