Evaluation Options for Wildlife Management and Strengthening of Causal Inference
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.
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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 | 0.352 | 0.548 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".