An assessment of survey techniques using unmanned aerial vehicles to monitor Nile crocodiles (<i>Crocodylus niloticus</i>)
Bibliographic record
Abstract
Monitored populations of the Nile crocodile Crocodylus niloticus at the southern end of its distribution, in the KwaZulu-Natal province of South Africa, are largely in decline. Trophy hunting of wild Nile crocodiles is only permitted at Pongolapoort Dam in the province, and monitoring of this population is required to enable the setting of annual hunting quotas. The aims of this study were to determine the feasibility of using drones to count and measure Nile crocodiles in the inlet to the dam and evaluate the utility of photomosaics, individual photographs, and videos for this purpose. A total of 16.5 km of shoreline was surveyed and 183 sub-adult and adult crocodiles observed, averaging 10.74 crocodiles per kilometer. The use of drones was cost-effective compared to traditional survey methods even though a higher number of person hours were required for data collection and processing. We recommend that drones be used to acquire video footage, supplemented by photomosaics in areas where large aggregations of crocodiles occur, to regularly monitor this crocodile population.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".