The prospects for the use of drone technology in the avian ecology research in Indonesia
Bibliographic record
Abstract
Abstract Drone technology has been expanding very fast in forestry and wildlife management, mainly for studying land-use, resource inventory, and wildlife population. The aim of the paper was to explore the prospects for the use of drones for studying wild birds in the tropical country of Indonesia. An intensive comparative literature study was performed, and a trial of behavioral study on a waterbird species was conducted. Drones for studying wild birds have been intensively used in the United States-Canada, Europe, and Australia. Common research topics were population study (especially at the difficult-to-reach habitat such as in Alaska, vast wetlands), monitoring (mainly breeding stages) and habitat selection. Large-sized birds nesting at the open area were excellent research objects. In Indonesia, very few trial research has been conducted, with waterbirds as target. Trial field study on the milky stork breeding behavior suggested that small drones can be used successfully. Challenges in the tropics were mostly weather condition (i.e., strong wind and heavy rain). The prospects to use drone for avian research in Indonesia is promising, although so far only targeted the large birds having a visible open nests such as waterbirds and raptors. Further research is needed to include many other bird groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".