Evaluation Options for Wildlife Management and Strengthening of Causal Inference
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 Wildlife management aims to halt and then reverse the decline of threatened species, to sustainably harvest populations, and to control undesirable impacts of some species. We describe a unifying framework of three feasible options for evaluation of wildlife management, including conservation, and discuss their relative strengths of statistical and causal inference. The first option is trends in abundance, which can provide strong evidence a change has occurred (statistical inference) but does not identify the causes. The second option assesses population outcomes relative to management efforts, which provides strong evidence of cause and effect (causal inference) but not the trend. The third option combines the first and second options and therefore provides both statistical and causal inferences in an adaptive framework. We propose that wildlife management needs to explicitly use causal criteria and inference to complement adaptive management. We recommend incorporating these options into management plans.
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 | 0.001 | 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