Some problems and solutions for the measurement of fish target strength: A study case with Atlantic redfish (<i>Sebastes</i> <i>spp</i>.)
Bibliographic record
Abstract
Potential biases in the measurement of redfish target strength (TS) were examined by comparing ex situ and in situ approaches. Ex situ experiments were conducted on individuals after developing a method to maintain live fish in good condition. The main problem with the manipulation of these species was the inflation and distortion of the swimbladder. To minimize this bias, fish were kept in sea cages after acclimation at depth and TS was measured in a camera-monitored apparatus set in proximity to the capture site. A series of in situ acoustic-trawl experiments was conducted on several aggregations of redfish in Newfoundland waters. TS were collected using a hull-mounted EK500 split-beam transducer and a deep-tow dual-beam system. The dual-beam transducer was calibrated at different depths to test if change in pressure and/or temperature affected its sensitivity. This system was used to measure aggregations of fish under different transducer depth. The data indicated that biases in in situ TS estimates increased with the range of observation, the density of fish, and the presence of multiple targets formed by the clustering of smaller organisms. Depending on the nature of the bias, TS can be over- or underestimated by as much as 6 dB.
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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 | 0.043 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.003 |
| 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".