Data Underpinning - Tuning the emission of cationic iridium (III) complexes towards the red through methoxy substitution of the cyclometalating ligand
Bibliographic record
Abstract
EZ-C acknowledges CFI (Canadian Foundation for Innovation), NSERC (the Natural Sciences and Engineering Research Council of Canada), FQRNT (Le Fonds québécois de la recherche sur la nature et les technologies) and the University of St Andrews for financial support. C. R-C. would like to thank the MINECO for the financial support of this research in the framework of project CTQ2010-17481, the Junta de Andalucía (CICyE) for special financial support (P08-FQM-4011 and P10-FQM-6703) and the MECD (Spanish Ministry of Education, Culture, and Sport) for an FPU grant. This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) (MAT2014-55200), the Generalitat Valenciana (Prometeo/2012/053). IDWS and AKB acknowledge financial support from the EPSRC of the UK (grant: EP/J01771).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.013 |
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".