Forecasting Payments Made under Construction Contracts
Bibliographic record
Abstract
Staff at the North Carolina Department of Transportation (NCDOT) have close control over each highway construction project until the construction contract is let; thereafter, the contractor manages the pace of work, which dictates the flow of payments. These payments make up about one-third of all NCDOT expenditures, so reliable forecasts are important in programming and in cash management. With no other means to predict the pace of construction through the duration of the contract, NCDOT must rely on statistical analyses of past payments to forecast future payments to contractors. Dye Management Group, Inc., which was retained by NCDOT to implement cash management strategies at the department, designed two statistical models of payments to contractors: the first to estimate payments on individual contracts, or the “payout curve,” and the second to estimate total payments made under all contracts in a month. The parameters in both models were initially estimated with data from 4,128 payments made under 336 highway construction contracts completed between August 2000 and June 2002. The first model achieved an adjusted R 2 of .93. Although it was useful to engineers in managing individual projects, the model required awkward specifications of seasonal effects to forecast aggregate cash flows. Seasonality was simply accommodated in the second model, which achieved an adjusted R 2 of .92. Dye Management Group and NCDOT have operated the second model for more than 2 years and, with a database that has grown to more than 11,000 monthly payments, have consistently achieved mean absolute percentages of error under 10%.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".