Examining the utility of alternative video monitoring metrics for indexing reef fish abundance
Bibliographic record
Abstract
Underwater video has become an important tool for monitoring reef fish populations worldwide because it is nonextractive and not strongly selective. A variety of approaches have been developed to enumerate fish on videos, but to our knowledge these metrics have not been tested to determine if they are proportional to true abundance. We compared the most commonly used metric, MaxN (i.e., the maximum number of fish in a single frame during the viewing interval), to a newly developed metric, MeanCount (i.e., the mean number of fish observed in a series of snapshots over a viewing interval), using simulations, a laboratory experiment, and an empirical study. MaxN was nonlinearly related to true abundance using all three approaches, providing increasingly dampened estimates of abundance with increasing true abundance (i.e., hyperstability). Therefore, MaxN may result in positively biased indices of abundance for declining fish stocks or negatively biased abundance indices when fish stocks are increasing. Alternatively, MeanCount was generally linearly related to true abundance and its variability was similar to MaxN, suggesting that MeanCount can be useful for indexing abundance of fish in underwater video surveys.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".