Speed of sound gradients due to summer thermal stratification can reduce the detection range of acoustic fish tags: results from a field study in Hamilton Harbour, Ontario
Bibliographic record
Abstract
Understanding detection range is a key factor for the use of acoustic telemetry in fisheries research. Lakes have strong seasonal changes in thermal stratification, as well as short-term changes due to internal seiches. These thermal gradients in lakes imply strong sound-speed gradients that can refract and diverge acoustic signals, leading to acoustic attenuation and smaller detection range. Using field-based range testing and the Bellhop acoustic model, we investigated how changes in stratification lead to changes in detection range within Hamilton Harbour, Ontario, Canada. During the summer stratified period, the detection range was less than 350 m, whereas in the isothermal fall, range was up to 500 m. Range test data from three separate field observations showed a good correlation with Bellhop predictions. Owing to the intense internal seiches in Hamilton Harbour, the stratification in the shallower littoral regions essentially switched between stratified and isothermal conditions over short timescales, which is predicted to lead to high temporal variability in detection range that must be accounted for during the analysis and interpretation of telemetry-derived data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".