The Gain Reduction Method for manual tracking of radio-tagged fish in streams
Bibliographic record
Abstract
Manual tracking has been used since the 1970s as an effective radio telemetry approach for evaluating habitat use of fish in fluvial systems. Radio tags are often located by continually reducing the gain when approaching the tag along a watercourse to estimate its location, termed here as the ‘Gain Reduction Method’. However, to our knowledge the accuracy of this method has not been empirically evaluated and reported in the literature. Here, the longitudinal and lateral positional errors of radio tags are assessed when applying the Gain Reduction Method in a small stream environment. Longitudinal and lateral positional errors (i.e. the difference between the estimated and actual radio tag location) were evaluated based on the distance from the actual tag position, the gain recorded when estimating the tag position and a number of environmental parameters (i.e. stream depth, velocity, stream width and specific conductivity). The manual tracking trials produced an average lateral positional error of 0.91 m (± 1.4) and a longitudinal positional error of 0.66 m (± 0.87). A larger degree of longitudinal positional error was documented when the gain was higher ( t = 2.21, p < 0.05). Larger lateral positional error was recorded when the tag was farther across the stream ( t = 2.27, p < 0.01) and due to greater inaccuracy in longitudinal positioning ( t = 3.2, p = 0.001). In addition, greater rates of lateral positional error were found when specific conductivity levels were higher ( t = 2, p < 0.05). Longitudinal and lateral positional errors were not influenced by stream width (m), depth (m) or velocity (m/s). Although the Gain Reduction Method is commonly used to estimate habitat use of stream fishes, there appears to be a paucity of information in the literature that addresses the accuracy for obtaining fine-scale positioning of tagged fishes. This study is aimed to address this knowledge gap by identifying sources of locational error with the Gain Reduction Method. Overall, habitat variables were deemed to be unlikely to have a significant effect on estimating fish position in small streams. Researchers should be aware that error in the longitudinal direction will translate into larger errors in the lateral position. Further exploration of positional accuracy using this active tracking approach is recommended for larger and deeper fluvial systems.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".