The Wildlife Picture Index: monitoring top trophic levels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Although recent biodiversity loss has been compared with cataclysmic mass extinctions, we still possess few indicators that can assess the extent or location of biodiversity loss on a global scale. The Convention on Biological Diversity (CBD) has mandated development of indicators that can meet the needs of monitoring biodiversity by 2010. To date, many indicators rely on unwarranted assumptions, secondary data, expert opinion and retrospective time series. We present a new biodiversity indicator, the Wildlife Picture Index (WPI) that targets medium and large‐sized terrestrial birds and mammals in forested and savannah ecosystems that. The WPI is a composite indicator based on the geometric mean of relative occupancy estimates derived from camera trap sampling at a landscape scale. It has been designed to meet the needs of a CBD indicator while avoiding many of the pitfalls that characterize some CBD indicators. We present an example using 8 years of camera trap data from Bukit Barisan Selatan National Park, Indonesia to show that the WPI is capable of detecting changes in the rate of loss of biodiversity, a key requirement of a CBD indicator. We conclude that the WPI should be effective at monitoring top trophic levels in forest and savannah ecosystems using primary data and can fill the gap in knowledge about trends in tropical biodiversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 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