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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it