Investigating the impact of operating parameters on molecular weight distributions using functional regression
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 Molecular weight distributions (MWDs) are inherently functional observations in which differential weight fraction is expressed as a function of chain length. Conventional approaches for analyzing and predicting MWDs include discretization and treatment as multi‐response estimation problems, characterization using moments, and detailed mechanistic modeling to predict fractions for each chain length. However, these approaches can be sensitive to loss of information, complexity and problem conditioning. An alternative is to treat the MWDs as functional observations, and to use techniques from Functional Data Analysis (FDA), notably functional regression. The objective of this paper is to develop and apply empirical modeling techniques based on functional regression for investigating the impact of operating parameters on MWDs.
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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 | 0.000 | 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