Qualitative & Semi-Quantitative Reasoning Techniques for Engineering Projects at Conceptual Stage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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