Rapid assessment of wildlife abundance: estimating animal density with track counts using body mass–day range scaling rules
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Limited resources in conservation dictate the need for efficient means of assessing wildlife abundance. Body mass–day range scaling rules and empirical track counts were applied to an established formula to estimate a wide range of wildlife densities. Using the southern K alahari ecosystem of B otswana as an example, I provide the first comprehensive density estimates for the mammalian wildlife community (>0.2 kg), including densities for several species previously unattainable by other methods. Among a subset of species, empirical day ranges from this area were consistently greater than those predicted using scaling rules modeled with species from diverse ecosystems. I applied a correction factor based on this discrepancy, which generated values congruent with independent density estimates from the area. Although accurate measures of day range are a practical constraint to estimating densities from track counts, the results suggest that modest efforts to obtain location‐specific day range estimates for a subset of species can improve density estimates for others derived from general allometric relationships. Given the strength of track surveys to accumulate unbiased observations quickly, in environments where animal tracks are readily visible, this approach shows potential for the rapid assessment of wildlife abundance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it