Apportioning Concurrent Delays and Accelerations Using Daily Windows
Bibliographic record
Abstract
Project as-built duration is the resultant of all day-to-day events and actions, including slowdowns, work stops, and accelerations, made by all project parties. In current practice, however, a systematic procedure for recording and analyzing daily actions is lacking, thus making the quantification and analysis of time-related and cost-related claims a complex task that is highly controversial. In this paper, a practical model is presented, with an analytical framework, for analyzing project as-built schedules, considering slowdowns, work stops, and accelerations. The model differentiates between owner-directed and contractor-voluntary accelerations and deals with acceleration as a negative delay attributable to the party that creates it. To provide accurate and repeatable results, the model uses a daily windows analysis technique for apportioning concurrent delays and accelerations. Details of the proposed model are provided along with an example application. The model is readily usable by professionals and researchers to dynamically analyze the impact of all events along project duration.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".