Comparing alternative budget allocation models to support strategic wildland fire program analysis across US National Parks
Bibliographic record
Abstract
Hazard fuel reduction and wildland fire preparedness programs are two important budgeting components in the US National Park Service strategic wildland fire planning.During the planning process, each national park independently conducts analysis to understand the benefits from investing in each program to mitigate fire risks and improve ecosystem benefits.The national program analysis imports the cost-effective frontiers of investment in both programs from each national park.The national program then allocates cost-effective funding to the parks and implements required national policies while minimizing disruption to current programs of work.In this study, we test and compare two alternative modeling methods for budget allocation between the fuel treatment and preparedness programs responding to changes in funding levels nationally.One approach uses a nonlinear programming model (NLP) to maximize the benefits of investments in both programs with a set of feasibility constraints.The other approach uses a simulation-based gradient method to manage program budget changes.Both approaches are designed to focus on national level program efficiency while mitigating potential program disruptions; however, different approaches suggest different budgeting allocation strategies.This study compares the trade-offs between efficiency and the level of disruption of different budget allocation methods.Discoveries could help managers to select and implement an efficient and viable analytical system to study the value of funding increases, the cost of budget reductions, and guide landscape allocations.It will also identify national impacts by accumulating allocations to individual units across the national parks in the United States.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".