Bibliographic record
Abstract
Material can be seen as the fuel needed to execute the project from inception to completion. Material installed provides good indicators of progress achieved onsite vis-a-vis project schedule performance. It correlates well with the role of the schedule performance index (SPI) of the earned value method (EVM). Material is recognized to have a significant impact on achieved progress for physical completion of project activities. This paper presents a study on the development of material status index (MSI) in support of the EVM. Unlike the SPI, the newly developed index account for the criticality of project activities. This is carried out considering the total float of each activity, percent float (i.e. the ratio of float to activity duration) and the total float of the path on which the activity is located. MSI, can independently and jointly with SPI provide root causes behind problems encountered during project execution. In turn MSI can reveal material related factors behind the performance detected by joint interpretation of the two indices. MSI serves to provide added value in alerting management to take corrective actions. While SPI and MSI may have different values, they can jointly augment and enrich the captured project status based on EVM. To demonstrate the capabilities of the developed MSI method, it is implemented on a case study. The case encompasses the construction schedule of a hydro power station constructed in northern Quebec. Different scenarios are adapted from the real case to demonstrate a set of practical aspects of the developed index. The results generated from the analysis of the case study illustrate the useful features of MSI beyond those of the traditional SPI on two fronts; causation and the considerations of criticalities of actives.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".