Testing marine conservation applications of unmanned aerial systems (UAS) in a remote marine protected area
Bibliographic record
Abstract
In 2014, the United States National Oceanic and Atmospheric Administration (NOAA) utilized unique partnerships with the National Aeronautics and Space Administration (NASA), and the US Coast Guard for the first comparative testing of two unmanned aircraft systems (UAS): the Ikhana (an MQ-9 Predator B) and a Puma All-Environment (Puma AE). A multidisciplinary team of scientists developed missions to explore the application of the two platforms to maritime surveillance and marine resource monitoring and assessment. Testing was conducted in the Papahānaumokuākea Marine National Monument, a marine protected area in the Northwest Hawaiian Islands. Nearly 30 h of footage were collected by the test platforms, containing imagery of marine mammals, sea turtles, seabirds, marine debris, and coastal habitat. Both platforms proved capable of collecting usable data, although imagery collected using the Puma was determined to be more useful for resource monitoring purposes. Lessons learned included the need for increased camera resolution, co-location of mission scientists and UAS operators, the influence of weather on the quality of imagery collected, post-processing resource demands, and the need for pre-planning of mission targets and approach to maximize efficiency.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".