Bibliographic record
Abstract
Contractors are frequently required to provide detailed schedules soon after award of contracts. Owners are to evaluate and subsequently approve these schedules. The approved schedules are then used to generate project's baselines; necessary for tracking and progress reporting as well as administration of construction disputes. As such, it is important to insure the goodness of these schedules. This paper provides a structured methodology to assist owners in performing such schedule assessment and evaluation. In essence, the developed methodology serves as a check list that covers a set of overall requirements for good schedules. The methodology is based on integration of scattered knowledge. The developed methodology has been implemented in automated computer application encompassing three tiers of schedule assessment to facilitate effective evaluation of detailed schedules. This is particularly useful in performing schedule assessment of large projects, which have hundreds, if not thousands, of activities and may involve owners' participation in schedule development. This paper provides an overview of the developed system and describes its basic components. An actual project schedule is analyzed to illustrate the essential features of the computer application. The developed application can also be helpful to contractors; serving as guideline and recommended practice in scheduling.
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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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".