Predicting factors affecting likelihoods in engineering problems
Bibliographic record
Abstract
A method of predicting the factors which most affect the likelihoods of specified events from data bases or tables of data is presented. This method is called the Variable Precision Rough Sets Model with Asymmetric Bounds. The data is broken into a limited number of meaningful ranges. The tables of data are then reduced to a minimum set of variables and rules for predicting the likelihood of the specified event. The method has been developed in a mathematically precise form. The methodology determines the best variables and data patterns to categorize the data in terms of the likelihood of a given event. This procedure has been programmed and tested on a steel industry problem. However, the procedure is applicable to any problem where an outcome or event is to be predicted from a set of known variables provided the data is available in a tabular or data base form. Such problems would include any failure or reliability problem in power, control or electronic systems. Another problem to which this method could be applied is that of predicting which input variables most affect the output variables in a complex control system.
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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 | 0.009 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".