One size does not fit all: a novel approach for determining the Realised Viewshed Size for remote camera traps
Bibliographic record
Abstract
Abstract 1. Camera traps (CTs) have become cemented as an important tool of wildlife research, yet, their utility is now extending beyond academics, as CTs can contribute to more inclusive place-based wildlife management. From advances in analytics and technology, CT-based density estimates of wildlife is an emerging field of research. Most CT-based density methods require an estimate of the size of the viewshed monitored by each CT, a parameter that may be highly variable and difficult to quantify. 2. Here, we developed and tested a standardized field and analytical method allowing us to predict the probability of photographic capture as it varies within CT viewshed. We investigated how capture probability changes due to environmental influences, i.e., vegetation structure, ambient temperature, speed of subject, time of day, in addition to internal factors from CTs themselves, i.e., sensitivity settings, number of photos taken, and CT brand. We then summarize these spatial capture probability kernels into a Realised Viewshed Size (RVS)—the capture-probability corrected size of a CTs viewshed 3. We found that RVS values are heavily influenced by location-specific environmental factors, i.e., vegetation structure, technological delays associated with CTs themselves, i.e., refractory period, and internal CT settings, i.e., sensitivity, number of photographs taken. We also found that the RVS values computed using our methodology are substantially smaller than reported values in the literature. 4. Imprecision surrounding CT viewshed areas can create propagating bias when implementing CT-based density estimators. Our method can change how practitioners consider photographs for use in CT density estimators thus increasing the reliability of CT-based density estimation, and contribute to more accessible wildlife management practices.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".