Bibliographic record
Abstract
An overview of a model-based troubleshooting program that incorporates a domain-independent diagnosis engine based on J. de Kleer and B.C Williams' General Diagnostic Engine (Artificial Intelligence, vol.32, no.1, p.97-130, April, 1987) is presented. The primary input to the program is a model of a digital circuit that is a network of components and connections. Each component has a description of its dynamic time-dependent behavior and each connection transmits signals between components. The secondary input to the program is a description of the stimuli presented to the circuit and observations of its actual responses. The model uses those stimuli to predict what the outcomes of observations ought to be. When discrepancies are discovered, the program produces a list of components that could be responsible for the discrepancies, ranked by their relative likelihood. The program interactively suggests what observations should be made next in order to discriminate among these possibilities, then uses the new observations to incrementally focus on the correct diagnosis. Eight modeling principles broken up into three sets are discussed. One set of principles concerns how the structure of a given circuit should be represented. A second set of principles concerns the representation of circuit behavior. The final set of principles concerns what knowledge about failures should be represented explicitly.>
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".