Evaluating the role of fish behavior in surveys conducted with underwater vehicles
Bibliographic record
Abstract
It is often assumed that visual survey data provide more accurate fish counts than conventional extractive gear. As a result, use of underwater vehicles to assess the abundance and distribution of fishes has increased rapidly over recent years. However, a review of observations reported for 48 demersal marine fish taxa showed that almost all respond in some way to underwater vehicles. Whether or not movements or changes in behavior affect survey bias is more difficult to assess. A simple conceptual model is presented to evaluate relationships between stimulus intensity, distances from the vehicle where reactions occur, and survey bias. Largest bias is caused by attraction or avoidance that occurs outside the field of cameras or observers. While light level and vehicle speed have been explored experimentally in a few cases, much remains to be learned about how bias varies among species, age groups, different vehicles, and operating conditions. Given poor understanding of survey bias, we recommend that surveys be conducted with minimum possible variation in operations and that vehicle time is devoted to experimental evaluation of methods. There is no good substitute for direct observations on fish behavior, distribution, and abundance; and survey design can be improved through experimentation.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".