How does a new set of Earned Value Management schedule control work? A case study in IRAN
Bibliographic record
Abstract
One of the most effective project time and cost controlling systems is called Earned Value Management (EVM). This system is applied worldwide in different projects of many kinds. Australia, United States, Canada, United Kingdom, Sweden and Japan were the pioneers of applying this system and new reports show that other countries are joining this list. EVM metrics are the three primary concepts of planned, accomplished and actual work, which are integrated measures of time and costs. A number of researchers have found that the time metrics didn't judiciously refer to the schedule performance of a project. One of the recent improvements to the EVM is the application of new time metrics (Schedule Variance (time) (SV(t)) and Schedule Performance Index (time) (SPI(t))), which are based on time units instead of monetary units. A 15-month Iranian pipeline project, called “Ardak-Mashad Water Supply”, was controlled by the EVM in this paper. The stages of applying the EVM in this project are described and the difficulties that the EVM team encountered are also presented. In addition, the paper attempts to clarify the application of common time EVM metrics and compare them with the new set of time metrics to interpret the schedule performance of a project. All satisfactory results of the EVM
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".