The STP model for solving imprecise problems
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
Abstract — Researchers have been attracted for years to studies on solving imprecise problems. The first step for solving an imprecise problem is to clarify the problem itself. However, in many of cases, the impreciseness of a problem is due to its own nature and often leaves them unsolvable. There are at least two reasons for the impreciseness and unclearness of a problem. The first reason is that there may not be a suitable language to present the problem in an understandable and clear way. The second reason is that the problem itself is not well-definable. This is quite similar to a research question that a researcher is trying to specify. A problem may only be fully understood and specified after all solutions are available. In this paper, we introduce a problem solving approach by searching possible solutions to an imprecise problem. This is an approach to specify and solve an imprecise problem by matching the problem with its solutions. We present a Solution-To-Problem (STP) model as a new approach for imprecise problem solving. Basic notions, measures and algorithms for such a problem solving process are studied. I.
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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 | 0.001 | 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