The Integrated Deconvolution Estimation Model: Effect of Inter‐Laboratory <sup>13</sup>C NMR Analysis on IDEM Performance
Bibliographic record
Abstract
Abstract The IDEM estimates the reactivity ratios of multiple‐site‐type catalysts used to make ethylene/α‐olefin copolymers. Analytical data from high‐temperature GPC and 13C NMR are required in the estimation process. The CSLD information from the 13C NMR is a crucial step in the estimation method due to NMR sensitivity and probe efficiency. The effect of inter‐laboratory analysis on IDEM parameter estimation and model predictions is studied. The copolymer samples are analyzed at the University of Waterloo and Dow Chemical Research Center at Freeport, Texas without standardization. The results prove that the IDEM is a robust parameter estimation model for ethylene/α‐olefin copolymers made with multiple‐site‐type catalysts, even when the copolymer samples are analyzed in different laboratories. magnified image
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".