APPLICATION OF THE ADAPTIVE MATRIX FILTER TO INVERSE HEAT CONDUCTION 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 A matrix filter has been developed to remove errors from temperature measurements in the field of heat conduction. It has been shown that the matrix filter substantially reduces the errors in measurements. The filter extended to a new adaptive matrix filter (AMF) enables identification of some thermal properties based on the temperature measurements of the system involved. The approach allows elimination of many problems associated with the solution of nonlinear equations. AMF can use data containing errors; this allows for a simultaneous estimation of unknown properties and removal of errors from measurements. It has been proved that the algorithm is very efficient. Keywords: adaptive filter matrixheat conductioninverse problemnoisy datanumerical analysissystem identification
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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