Possible Criticality of Paths in Networks with Imprecise Durations and Time Lags
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Bibliographic record
Abstract
This paper presents a new fuzzy model for possibly critical paths in the networks with imprecise durations and time lags. We have modified the former presented approaches for this problem by inserting the fuzzy time lags in the scheduling of the projects. To model and solve the fuzzy problem, we first used interval numbers. In fuzzy arithmetic, usually the interval calculations are used for the aim of complexity reduction and simplification. Then, we generate a fuzzy network for project scheduling, where both durations and time lags are fuzzy. To determine the degree of possibly critical paths, we use a linear programming (LP) model. Finally, we test and validate the proposed fuzzy model by presenting a numerical example. The results show that the proposed model is more robust and reliable than the previously generated methods in this area.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it