Experimentally Determining Optimal Conditions for Mapping Forage Fish with RPAS
Bibliographic record
Abstract
RPAS (Remotely piloted aircraft systems, i.e., drones) present an efficient method for mapping schooling coastal forage fish species that have limited distribution and abundance data. However, RPAS imagery acquisition in marine environments is highly dependent on suitable environmental conditions. Additionally, the size, color and depth of forage fish schools will impact their detectability in RPAS imagery. In this study, we identified optimal and suboptimal coastal environmental conditions through a controlled experiment using a model fish school containing four forage fish-like fishing lures. The school was placed at 0.5 m, 1.0 m, 1.5 m, and 2.0 m depths in a wide range of coastal conditions and then we captured RPAS video imagery. The results from a cluster analysis, principal components, and correlation analysis of RPAS data found that the optimal conditions consisted of moderate sun altitudes (20–40°), glassy seas, low winds (<5 km/h), clear skies (<10% cloud cover), and low turbidity. The environmental conditions identified in this study will provide researchers using RPAS with the best criteria for detecting coastal forage fish schools.
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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".