Evaluation of an unmanned rotorcraft to monitor wintering waterbirds and coastal habitats in British Columbia, Canada
Bibliographic record
Abstract
The effective protection of coastal and estuarine habitats requires reliable monitoring information on their use by waterbirds, and the use of small unmanned aircraft systems (UAS) may provide access to these habitats without disturbing birds. We evaluated the use of a rotary-wing UAS with a high-end consumer camera to identify and count wintering waterbirds at two coastal sites in British Columbia, Canada, in January 2015, and to map mudflat and marsh habitats. Photos of shorebirds, waterfowl, and seabird species were taken at varying altitudes, and disturbance of birds appeared minimal when the UAS was flown at heights ≥61 m. A ground resolution of ~1 cm/pixel was needed to discern plumage characteristics necessary to reliably identify birds. For some duck species, identification of females relied on body size or close association with a nearby male. Photographs were also used to derive accurate counts of shorebirds. For diving birds, accurate counts from photographs will require information on the proportion of birds on the water surface. Orthomosaics of coastal habitats were constructed with sufficient detail to assess ecological and geomorphological features. The UAS can therefore assist with bird species identification, population assessment, and characterization of habitat types.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".