Bibliographic record
Abstract
Fault tolerance in science and engineering so far was considered as a property of a system. If we think a bit deeper, it can be discovered that it is actually a process, that has to be pursued from the conceptual, design phase of a project up to the complete end and scrap value of the product. To deal with this process, we have introduced A Generalized Algorithm of Fault Tolerance. An implementation analysis of this algorithm - if we make it rigorous - might be useful for analysis of complex systems like a health care. Various options of dealing with diseases, along proposed algorithm, diagnosis and treatment (recovery), separation of diseases in terms of lethality, enable to analyze their different “power” at various steps. Implementation of an algorithm as a tool to analyze efficiency of healthcare efforts enable to create a quantifiable analysis of overall performance of healthcare system. A technical concept of fault tolerance is proposed, which should be applied for the analysis of health care and health monitoring. Along the text, when possible, health maintenance was discussed in terms of a technical system. This approach, when applied in the future, might become a core of analysis, rigor analysis of health systems and health management. This work is just the first step and invitation to researchers from related domains to start thinking about the path suggested.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".