Pressure sensor calibrations of acoustic telemetry transmitters
Bibliographic record
Abstract
Acoustic transmitters are widely used to obtain information on the spatial ecology of fish and other aquatic animals. Some transmitters contain pressure sensors to estimate depth, which are factory-calibrated before being sold and have a specified range of error. Our goal was to assess the accuracy of these pressure sensors and the factory calibrations to assess whether researchers should conduct additional calibrations prior to use in the field. To evaluate error, we conducted calibrations on ten acoustic transmitters with pressure sensors (obtained from Vemco-Amirix Ltd.) both in the laboratory (pressure chambers at Hammond Bay Biological Station and Carleton University) and in the field (based on lowering tags to known depths in Toronto Harbour and Experimental Lakes Area). Slopes, intercepts, and R 2 values of researcher-calibrated sensors were compared to the factory-calibrated values to contrast calibration methods and identify directional biases. To estimate the effects of temperature on sensor performance, we calibrated the same sensors at varying temperatures and compared slopes, intercepts, and R2 values. Finally, we evaluated external effects (i.e., water temperature, salinity, and atmospheric pressure) on sensor output through simple modeling exercises to better understand potential sources of error. A significant difference was found among the slopes and R 2 values of the four calibration events, whereas no difference was found among the intercepts. There was also a significant effect of calibration water temperature on slopes, intercepts, and R 2 values. External effects should be taken into consideration when interpreting biological data as they have an effect on hydrostatic pressure thereby affecting the reported depths (1.77 m shallower to 6.47 m deeper than standard conditions). Nonetheless, we did not find sufficient evidence to support the need for additional calibrations beyond those provided by the manufacturer as they did not markedly increase the accuracy of depth estimates.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".