Dude, Where's my Transmitter? Probability of Radio Transmitter Detections and Locational Errors for Tracking River Fish
Bibliographic record
Abstract
Abstract To simulate radio-location tracking of fish in a lotic system, we deployed 148-MHz radio transmitters and measured signals from three altitudes over a known location. We examined detection distance, location error, and detection probability for two transmitter types at seven transmitter depths. We found that transmitter type, altitude, flight direction, and depth affected both detectability of transmitters and accuracy of locations. Maximum observed detection distance was 5,614 m for the small-type transmitters at an altitude of 300 and 14,508 m for large-type transmitters at an altitude of 600 m. Detection distances declined rapidly with increasing transmitter depth for both transmitter types. Locational errors ranged from 8 to 842 m (x¯ = 177 m; SE = 15.2) and were biased with flight direction. Detection probabilities declined with increasing transmitter depth and with increasing number of scanned frequencies. Scanning five frequencies, at the optimal flight altitude for a given transmitter type, resulted in nearly a 50% loss of detection probability at a 5-m depth and a 90% decrease at a 7-m depth. We recommend that researchers model their probability of detection a priori, all transmitters transmit on a single frequency, and a receiver altitude of 300 m should be maintained.
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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 | 0.005 | 0.038 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".