Passive Integrated Transponder (PIT) Tracking versus Snorkeling: Quantification of Fright Bias and Comparison of Techniques in Habitat Use Studies
Bibliographic record
Abstract
Abstract Quantitative assessment of day and night fright bias (i.e., flight response) of Atlantic Salmon Salmo salar parr during passive integrated transponder (PIT) tracking surveys was carried out during summer (water temperature, 18–22°C) and autumn (water temperature, <3°C). In addition, PIT‐tracking and snorkeling survey methods were compared to assess whether the two methods result in similar habitat use data for Atlantic Salmon parr at the same study site. During summer fright bias surveys, 0–15% of parr displayed a flight response to PIT‐tracking techniques in the riffle–run–pool habitat types commonly used by Atlantic Salmon parr; 24–25% displayed a flight response in relatively unused, shallow, calm water habitats. No flight responses were observed in autumn surveys with colder water temperatures in any habitat type. Larger numbers of salmon parr were observed using PIT tracking regardless of stream discharge. Furthermore, significant differences in habitat use frequency curves between these two methods were observed due largely to higher Atlantic Salmon parr abundances being detected by PIT tracking in shallow water depths at lower discharges and the ability to detect inactive salmon parr hiding within the substrate. PIT tracking was found to be a valid method for habitat assessment and provides more reliable habitat use data than traditional snorkeling methods in small streams.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".