A study of overlapping and functional interaction mechanisms for concurrent engineering processes
Why this work is in the frame
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Bibliographic record
Abstract
This article reports the use of a stochastic computer model to study hybrid overlapping and functional interaction strategies, where, within a given process, different degrees of overlapping or gradually increasing or decreasing functional interaction were modeled. The study aims to understand the contribution of these strategies to process performance, that is, product development effort and span time. Simulation results of the hybrid models are discussed in comparison to a baseline model, where the baseline process was uniformly overlapped and functional interaction was constant throughout its execution. Research outcomes indicate that under high information uncertainty, sequential processes perform better than any model with overlap. When uncertainty is moderate or low, the baseline model outperforms the hybrid models. Under high sensitivity conditions, hybrid overlapping models perform equally well in comparison to the baseline model with complete overlap, and superiorly when information evolution is slow.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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