Validation of Environment Based Design (EBD) through Applications of Design Chain Management and Quality Management System
Bibliographic record
Abstract
Validation of Environment Based Design (EBD) Methodology through Applications of Design Chain Management and Quality Management System Xuan Sun Environment Based Design (EBD) is a recursive design methodology including three interdependent design activities: environment analysis, conflict identification and solution generation.EBD gives detailed instruction and provides useful tools in each step, so designers can easily apply EBD in different fields.Also, EBD can give designers a sense of right direction by guiding them collect the necessary and sufficient information from existing environment and customer requirements, which help designers focus on the creative activities.This thesis aims to validate the effectiveness of EBD methodology based on two case studies.One is the formalization of design chain management (DCM).A formal conceptual model for DCM is generated from its informal definition by applying EBD.This effort is different from other existing approaches to developing conceptual models in that the model is derived step by step from the natural language description of the DCM.The logical and interpretable formalization process of DCM shows EBD is an effective design methodology.In the second case, EBD is adopted to develop a Quality Management System (QMS) and generate a quality manual for an environment monitoring service.The challenge was that the content and structure of the final manual iv were not clear to the customer.By taking this task as a design problem, EBD helps get the real customer requirements from a fuzzy description and develop the QMS by solving the root conflicts.This application of EBD shows the effectiveness of the EBD as a generic design methodology.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".