A Resource Allocation Modelfor Tiger Habitat Protection
Bibliographic record
Abstract
Conservation of habitats is critical for survival of endangered tigers. This paper develops a resource allocationmodel for tiger habitat protection incorporating information about threats to particular tiger subspecies, thequality of remaining habitat areas, the observed effectiveness of habitat protection by country, and the potentialcosts of protection projects for 74 habitats in Asia. Implementation of the model moves through two stages. Thefirst stage employs user-specified weights to combine numerous subindices into composite indices of speciesthreat, habitat quality, potential project costs and protection effectiveness. The second stage employs additionaluser-specified weights to combine the composite indices into priority scores and potential project budget sharesfor all 74 habitat areas.Exploration of model results suggests that changes in user-specified weights can have very significantconsequences for habitat priority scores. Illustrative scenarios indicate that no single priority ordering can beprescribed in such a diverse setting, and actual priorities will depend on the preferences of decision-makers, asrevealed in the weights assigned to species threats, habitat quality, cost elements, and effective protection. At thesame time, the model can make a useful contribution by identifying priority orderings that are consistent withdifferent sets of preferences. And it can inform policy discussions by allowing for extended exploration ofalternative strategies, along with providing feedback to decision makers about the implicit preferences associatedwith their resource allocation decisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".