Manager-based valuations of alternative fire management regimes on Cape York Peninsula, Australia
Bibliographic record
Abstract
Decisions about fire management on pastoral properties are often made with little empirical knowledge. Proper accounting of the interactions between land, pasture, trees and livestock within the context of climatic variability and market conditions is required in order to assess financial implications of alternative fire management regimes. The present paper aims to facilitate such accounting through the development of a manager-driven decision-support tool. This approach is needed to account for variable property conditions and to provide direction towards considering optimal practices among a vast array of potential activities. The tool is an interactive model, developed for a hypothetical property, which analyses the costs and benefits of a baseline (no fires) against a historically based probability of wildfire overlaid by four alternative fire management regimes, representing cumulatively increasing levels of fire management intensity. These are: Regime 1, no action taken to prevent or stop wildfires; Regime 2, fire suppression (reactive fighting of wildfire); Regime 3, Regime 2 plus prevention (early dry-season burning); and Regime 4, Regime 3 combined with storm-burning (burning soon after the first wet-season storm). The model, which shows that fire and fire management have significant influences on the gross margin of Cape York Peninsula cattle properties, can be used as a decision-support tool in developing fire management strategies for individual properties. Specific fire management recommendations follow, together with the identification of potential areas of future work needed to facilitate use of the tool by clients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".