A Generalized Time-Scale Network Simulation Using Chronographic Dynamics Relations
Bibliographic record
Abstract
The scheduling information for complex and fast-track projects is often incomplete, and some decisions are postponed for a later date when new data is available. One solution is to propose several alternative task execution sequences, which could mitigate some uncertain and doubtful results. However, the use of non-time-scaled solutions prevents their integration into the existing construction planning software. This paper reviews and analyses the roles, advantages and disadvantages of the Temporal Function as proposed by the Chronographic Scheduling Method and introduces a generalized time-scale network simulation by means of production-based dynamics relations. The proposed solution uses decision points as part of the temporal functions which manage the uncertainty and their interdependencies. These functions are extended to represent the existing risks associated with an activity and its respective probabilities. The probabilities are represented by an entity that contains the most probable duration, and productivity values with their corresponding costs. This model is characterized by its relative ability to perform simulation studies based on the probabilistic aspect of the dynamic relationships and the streamlining of interactions between activities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".