Measurements of juvenile Atlantic bluefin tuna (<i>Thunnus thynnus</i>) size using an unmanned aerial system
Bibliographic record
Abstract
An APH-22 vertical-takeoff-and-landing hexacopter was used to collect aerial images of schools and individuals of juvenile Atlantic bluefin tuna (ABFT; Thunnus thynnus) at the sea surface in the southern Gulf of Maine. Quantitative measures of fish length, width, and inter-fish spacing were obtained from these images by applying calibration settings and performance measures from calibrating, testing, and evaluating the onboard motion and altimeter sensors and the digital camera and lenses. The accuracy and precision of the onboard motion sensors, camera, and lens calibrations were sufficient to provide length measurements to sub-centimeter precision, but the altimeter performance was least reliable and required additional information, such as images of known-sized objects during each flight, to provide measurements at the accuracy and precision needed for data to be incorporated in fisheries management. The APH-22 was ideal for acquiring images of ABFT individuals and schools and may be a useful tool for remotely monitoring the behavior and body condition of these elusive animals.
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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 | 0.000 |
| 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".