Towards the Visualization and Understanding of Diagnoses in a Physical System 1
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
Diagnosis of physical systems such as car or aircraft engines tends to be a complex activity. Technicians combine textual manuals with schematics and some analysis of measured data to diagnose and repair engines. Knowledge-based system designers have added heuristics to hypermedia (mostly text and graphic) representations of manuals to simplify the tasks of the technicians as in JETA [Halasz91]. Knowledge browsers for such systems are used by the knowledge engineers to structure and input the knowledge base and in a limited capacity are used to help the domain experts visualize the knowledge and the various possible relations as implemented for the JETA browser. In RATIONALE, a diagnostic system that reasons by explaining, explanation was used to understand the reasoning in the knowledgebased system [Abu-Hakima90]. This position paper argues that although these knowledge-based approaches help in the visualization and understanding of diagnoses in physical systems, they need to be improved and better integrated into an overall diagnostic system. Some of the many issues that arise in trying to achieve this are discussed in the paper.
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 | 0.000 | 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 | 0.000 |
| 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