Independent testing of PIT tags for fisheries research: a framework for standardization and performance evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Passive integrated transponder (PIT) tags are widely used to track animal movements and survival. Rigorous testing protocols are necessary to ensure reliability in PIT tag performance and resulting data across various environmental conditions. This study aimed to document a comprehensive testing framework for PIT tags as a model for the broader biotelemetry community and to showcase how independent evaluations can validate the performance of new PIT tag offerings against established regional performance criteria. Independent testing and adherence to regionally applied standards were key components of this effort. The Voda IQ HQ12, HQ10, HQ9, and HQ8 PIT tags were evaluated through a series of independent tests, including assessment of physical dimensions, electrical parameter testing, and proximity evaluations. The HQ10 and HQ9 tags passed all performance criteria, while the HQ12 tag excelled in most areas but exceeded the region's maximum weight threshold by 0.0022g. Despite this, the HQ12 tag showed strong detection efficiency and read range, particularly in challenging environments like the Bonneville Corner Collector. The HQ8 tags, while showing a more limited read range, offer advantages in applications requiring minimal tag burden. Independent testing played a crucial role in validating the performance of these tags under established protocols. This study underscores the importance of rigorous testing for PIT tags to ensure reliability across diverse environmental conditions. Independent evaluations like these not only inform stakeholders, but also encourage the adoption of new technologies and vendors. The methods and results presented here offer a valuable model for testing new biotelemetry technologies, applicable across different species, ecosystems, and monitoring programs worldwide.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it