Analysis of the prediction accuracy of the United States Navy repair turn-around time forecast model
Bibliographic record
Abstract
This thesis examines the forecast accuracy of repair times for a subset of repairable U.S Navy inventory items. Forecasts are currently calculated using the Uniform Inventory Control Program (UICP) on a quarterly basis. The UICP model use the time of repairs completed in the current quarter to update a "file" value in order to forecast the repair times for the following quarter. Forecasts are calculated separately for repairable items grouped into families. This thesis demonstrates that aggregation repairs by their completion dates, as currently done by the UICP model, causes forecast to be affected by the nature of the repair arrival process. The more that this process differs from a Poisson process, the more that the forecast values are affected. Using bootstrap simulations, the effect of the repair process on the forecasting is quantified. This thesis also explores alternatives to the UICP model for forecasting repair times. In particular, an approach that utilizes repairs that have not been completed by the end of the current quarter is developed.
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How this classification was reachedexpand
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.009 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".