Assessing acoustic receiver detection efficiency using autocorrelation adjusted machine learning models
Bibliographic record
Abstract
Detection efficiency is a key performance metric for acoustic telemetry arrays, providing an estimate of the probability of detecting a passing tagged organism. It is influenced by environmental (e.g., discharge), technological (e.g., transmitter power), and habitat (e.g., noise) factors, making predictions of detection efficiency a challenging task in the field of movement ecology. To predict detection efficiency, we applied regression-based machine learning models in two distinct river systems: a small mountainous and a large regulated river. The models incorporated daily discharge, water temperature and depth, substrate type, a receiver metadata metric indicative of noise, and the distance between receiver and acoustic tag. While both spatial and temporal autocorrelation were evaluated, only temporal autocorrelation required adjustment, which was addressed using a rolling cross-validation approach. Optimal cross-validation parameters differed between systems, with 30-day validation windows and 90-day steps for the large river, and 3-day validation windows and 5-day steps for the mountainous stream. Receiver distance and our utilization of receiver metadata as an indication of environmental noise consistently emerged as the most influential predictors, while environmental variables contributed relatively evenly to model performance. The small mountainous river model explained 30.7–89.5% of the variability in detection efficiency while the large regulated river model explained 43.8–90.6% of the variability explained. The model’s accuracy varied across resamples based on short rapid environmental changes during rolling cross-validation temporal binning. Our autocorrelation adjusted machine learning model demonstrated adequate estimates of detection efficiency, explaining an average of 68% of the variability across two distinct rivers. Restricted data availability in the mountainous stream and short rapid environmental changes in both systems presented challenges for model accuracy. Accounting for detection efficiency is an important component of describe animal movement using acoustic telemetry and our findings demonstrate machine learning models as an approach to predicting detection efficiency in acoustic receiver arrays across riverine environments with diverse hydrological and geomorphological characteristics.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".