Bibliographic record
Abstract
Project charter is perhaps the most important document included in the Project Management plan.It provides a preliminary outline of the project's scope, objectives and identifies the participants in the project.The schedule plan is responsible for bringing project time, cost and quality under control and links resources, tasks and time line together.Once a Project Manager has list of resources, work breakdown structure (WBS) and effort estimates, he is ready to go for planning project schedule.Schedule network analysis helps Project Manager to prevent undesirable risks involved in the project.Project Chain Management (PCM) and Radio Frequency Identification (RFID) are key elements of schedule network analysis.This paper presents a model of Multi-Agent System (MAS) dedicated to the PCM through RFID.It describes technical research on the troubles of privacy and security and explores solution for its problems using five phase agent models.MAS can interact to solve problems that are beyond the individual capacities or knowledge of problem solver.In the past several years, agent technology has played a central role in many application areas.It also provides a decentralized and adaptative approach for automated data capture and tracking in real-time which is a major constriction affecting the ability of stakeholders to optimize their investments in supply chain solutions.RFID combined with the MAS would be able to address these points and provide a range of benefits across various uprights.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.975 | 0.971 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".