Catalysis by organotransition metal compounds: Synergism between the pure and the applied<sup>1</sup>
Bibliographic record
Abstract
The modern era of transition-metal-catalyzed polymerization of alkenes began with the Nobel Prize winning work of Ziegler and Natta in the 1950s, but the field has exploded since the mid-1980s and anticipated applications of organometallic catalysts are being spectacularly realized. Our research in metal-catalyzed alkene polymerization began about ten years ago with an investigation of the catalytic applications of half-sandwich complexes of the Group 4 metals, and this lecture will describe our efforts to find both better initiators to make known commercial polymers and new initiators to make novel polymeric materials. Good luck, bad luck, blind alleys, and serendipity have all played key roles in our research, resulting ultimately in a very satisfying convergence of the motives for pure, and the needs of applied, research.Key words: alkene, polymerization, Ziegler, catalysis, carbocationic, titanium.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".