Qualitative & Semi-Quantitative Reasoning Techniques for Engineering Projects at Conceptual Stage
Bibliographic record
Abstract
During the development of engineering projects, the level of uncertainty is not static. The level of uncertainty typically diminishes from the early, conceptual stages of the project to the latter, detailed stages. At the present time there are many tools available to the engineer for reasoning with relatively low levels of uncertainty. Unfortunately there are few resources available for drawing sound conclusions from information that is characterized by a high level of uncertainty. Since decisions made early in the project cycle generally have a greater financial impact than those made later, it is worthwhile to investigate tools which are able to provide systematic and logical evaluation of preliminary or conceptual designs. This paper investigates sound techniques for evaluating projects at the early stages, including qualitative reasoning and semi-quantitative reasoning. The paper shows that qualitative analysis methods enable the engineer to reason with a high level of abstraction. As a normal engineering project progresses, more numeric information becomes available, and the results of semi-quantitative reasoning become more useful.
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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 | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".