Evaluation of an off-the-shelf Unmanned Aircraft System for Surveying Flocks of Geese
Bibliographic record
Abstract
Small off-the-shelf unmanned aircraft systems (UAS) could prove useful for surveying waterbirds. A low-end model was evaluated for surveying flocks of Canada Geese (Branta canadensis) and Snow Geese (Chen caerulescens) by comparing photographic counts from repeated flybys to repeated visual ground counts. Due to low contrast of Canada Geese with the ground, UAS counts based on confident detections only had a lower mean than ground counts for five out of six flocks (>30% lower for three flocks) and coefficients of variation (CV) ranging from 11–106%, compared to 1–6% for ground counts. Conversely, UAS counts of high-contrast Snow Geese were 60% higher on average and less variable (CV = 1–6%) than ground counts (CV = 11%). In some cases the aircraft likely detected birds that were not seen from the ground due to an obstructed view. Shortcomings of the UAS were mainly related to its unsophisticated imaging system compared to more expensive models. Otherwise, the UAS proved capable of being conveniently transported and deployed over flocks without causing them to flush. Further consideration should be given to off-the-shelf UAS for surveying waterbirds over small areas (<5-km radius) that are difficult to survey from the ground or as an option for performing low-disturbance surveys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".