Effects of radio‐transmitter antenna length on swimming performance of juvenile rainbow trout
Bibliographic record
Abstract
Abstract – Technological advances have lead to the production of micro radio‐transmitters capable of being implanted in fish as small as c. 5 g. Although the actual tags are small, transmitters are equipped with long antennas that can increase drag and tangle in debris. We examined the effects of radio‐transmitter antenna length on the swimming performance of juvenile rainbow trout, Oncorhynchus mykiss, (N = 156, mean mass = 34 g, mean fork length = 148 mm). Although we tested a variety of different antenna lengths up to a maximum of 300 mm, only the longest antenna significantly impaired swimming performance relative to control fish (P < 0.001). There was no difference in swimming performance between the sham (surgery, but no transmitter) and the control fish (handled, but no surgery), suggesting that the surgical procedure itself did not negatively affect the fish. Regression analysis, however, indicated that there was a significant decrease in swimming performance associated with increased antenna length (R2 = 0.11, P < 0.001). In addition, when held in laboratory tanks, fish with the three longest antennas (150, 225 and 300 mm) frequently became entangled with the standpipe. We suggest that researchers, under the guidance of the tag manufacturer, trim antennas to the shortest possible length required to detect fish in their specific study area. Antenna length is clearly an important issue for small fish, especially for species that inhabit complex habitats where antennas may become entangled, and where fish must attain speeds near limits of their swimming capacity.
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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 | 0.000 | 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) | 0.001 | 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".