Bibliographic record
Abstract
Abstract Polymerization data mining is the art of revealing insights and developing new knowledge from huge amounts of data routinely generated in polymerization systems and polymer characterization (polymerization processes and properties of polymer materials are the specific topic of this article). This becomes possible via development and implementation of robust and versatile intelligent data classifiers/clusterers for precise (numerical) processing of any given large theoretical/experimental datasets. Data mining is capable of effectively “cracking” recipe–microstructure–property interrelationships in modern macromolecular reaction engineering. This work offers a perspective, which contains a brief overview of the current state‐of‐the‐art and history of the area, along with current developments and trends in the data mining field (for polymerizations) with several conceptual examples. All in all, and similar to what is happening in other areas, polymerization data mining is becoming a necessity. The first applications seem promising. Applying molecular simulation approaches and artificial intelligence techniques, the design and establishment of powerful simulators for characterization and processing of virtually synthesized macromolecules are open to future developments, being of paramount importance to both industry and academia.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".