The Use of Earned Value in Forcasting Project Durations
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
This paper highlights the limitations of the current applications of EVM method in forecasting project durations and introduces a novel concept, embedded in an integrated method, in lieu of those currently in use. The proposed method is designed to improve the accuracy of forecasting, and can be used as an add-on utility to existing software systems that perform forecasting using the earned value method. The proposed method is based on a new formulation for the schedule performance index, which takes into consideration the concurrent nature of project activities in schedules and the manner used to generate cumulative progress. The main concepts behind the developed method are the use of "critical project baseline" and the use the status of critical activities only. A numerical example is presented to highlight the limitations of current forecasting methods and demonstrate the use of the proposed method and to illustrate its improvement of forecasting accuracy over current methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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