Impact of Consumption and Cost Forecasting on United States Defense Fuel Budgeting
Bibliographic record
Abstract
Between 2000 and 2011, Department of Defense (DOD) annual fuel expenditures were between $1 and $9 billion higher than budget estimates (excluding 2009, when DOD underestimated fuel expenditures). Fuel budget variance is generally attributed to increasing fuel prices. However, DOD fuel expenditures are driven by two parameters—the unit cost of fuel and the amount of fuel consumed. Cost variance was responsible for 80 percent of the fuel budget variance on average. Crude oil price increase drove most of this cost variance. Consumption variance was responsible for the remainder of the fuel budget variance, and was particularly important during initial wartime operations in Afghanistan and Iraq. Consumption variance was driven by DOD's planned use of emergency rather than base appropriations to pay for overseas contingency operations. Both increasing fuel prices and reliance on emergency appropriations puts defense operations at risk and increases costs to taxpayers. Improvements to current planning, budgeting, and financing practices are needed to manage this risk.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".