Population density of sitatunga in riverine wetland habitats
Bibliographic record
Abstract
Estimates of population density of mammals are critical data for effective management. Estimating density is complicated if the species of interest has cryptic markings and occupies dense habitat. Sitatunga is such a species, specially adapted to the dense swamps and marshes of sub-Saharan Africa, where traditional population survey techniques have been ineffective. In this study, we used camera traps to estimate density of sitatunga in central Uganda using both spatial capture-recapture methods and time in front of the camera (TIFC). We collected data in three years, 2015–2017. The TIFC model resulted in density estimates similar to the spatial capture-recapture models, without needing information on movement or individual identification. However, spatial capture-recapture models provide an estimate of movement and home range, which is of interest to management. For sitatunga, spatial capture-recapture models revealed higher movement parameters and higher heterogeneity in movement than previously reported. These results illustrate the utility of camera traps for a cryptic species in dense habitats, and provide a potential alternative to spatial capture-recapture methods.
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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 | 0.000 | 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 | 0.000 | 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".