Impact of Consumption and Cost Forecasting on United States Defense Fuel Budgeting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it