A preliminary study of multifunctional team design, modeling and formulation
Bibliographic record
Abstract
The t e m design method developed in this thesis for concurrent product design is based on three foundations: optimization formalisrn, game theory and hzzy set theory.Opmization formalisrn is applied to formulate some basic characteristics of tearn design, such as the design objective, constraints and availability of teams.Game theory is used to classify different types of team interaction as strategic team paradigms so that appropriate game solution concepts can be treated as team design protocols to guide the design process in certain tearn paradigms.Accordingly, the notion of responsibility and controllability is extracted to unify different team design protocois and used to develop the generalized tearn model.Fuzzy set theory is applied to model the team's preference toward a design, and fuzzy set operators are used for team's preference aggregation.Tearn aggregation is based on the strategic tearn paradigms derived fiom game theory.As a result, tearn design models are developed and explored to reveal typical team interactions in design cornputing. ACKNO
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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 | 0.000 | 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 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".