Bibliographic record
Abstract
In this thesis we propose a general framework for distributed diagnosis. Each diagnosis instance consists of two phases: local estimation, and inter-component communication for consistency. For the latter phase we introduce the concepts of supremal global support (for global consistency) and supremal local support (for local consistency). We provide a computational procedure CPGC for achieving supremal global support, and CPLC for supremal local support. The two supremal supports lead to distinct distributed diagnosis problems. It turns out that supremal global support results in better quality of diagnosis in the sense that fewer fault candidates are reported in each diagnosis instance; but supremal local support results in a computational procedure that is better scalable as long as it can terminate. In practice the two supremal supports may be combined for a satisfactory tradeoff between quality of diagnosis and scalability of the diagnoser. To reduce time complexity of CPGC, we propose a hierarchical computational procedure, utilizing multi-resolution diagnosis. Although high-level abstract models for hierarchical computation need extra memory, our numerical results show that the overall space complexity as measured by memory usage in storing both the models and the intermediate computational results is no worse (and in some cases better) than the space complexity in our non-hierarchical approaches. Finally, we explain how to use probabilistic reasoning to reduce diagnostic ambiguity without inserting extra sensors.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".