A field test of the use of pop‐off data storage tags in freshwater fishes
Bibliographic record
Abstract
freshwater system (Lake Ontario). The 9·3 cm long cylindrical tags were externally attached to fishes using a backpack-style harness, set to record pressure (dBar ≈ depth in m) and temperature every 70 s (and at some key times, every 5 s) and programmed to release from the harness and float to the surface after c. 1 year. Recapture of the bright-orange tags for data retrieval relied on members of the public finding tags on shore, or on anglers capturing fishes with tags attached and using the contact information displayed on each tag to mail tags to the research team in exchange for a monetary reward. Thirty-seven tags were found and returned from the 118 released (31%), while 26 of the 118 tags (22%) remained scheduled to pop-off in summer 2017. Of the 37 tags returned, 23 were from wild-caught fishes (out of 88 wild-caught and tagged fishes; 26%) and yielded useful data whereas 14 were from hatchery-reared fishes that were opportunistically tagged and appear to have been unable to acclimate to life in the wild and died days to weeks after release. The field study described here thus demonstrated that pDSTs can be a viable option for collecting large amounts of high-resolution depth and temperature data for salmonids in freshwater systems. Technical challenges, limitations and unknowns related to the use of pDSTs with freshwater fishes are discussed. In addition, pDSTs are compared with alternate electronic tagging technologies and assessed for their potential as a more widespread tool in research on freshwater fishes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".