Festschrift in Honor of Prof. Jean-Luc Brédas on His 65th Birthday
Bibliographic record
Abstract
Festschrift in Honor of Prof. Jean-Luc Bredas on His 65th Birthday I t is our great honor to dedicate this special issue of Chemistry of Materials to Professor Jean-Luc Bredas in honor of his 65th birthday.As a mentor, collaborator, colleague, and friend, Jean-Luc epitomizes many of the idealized qualities and worldview of the 21st century teacherscholar.It is this second descriptorcollaboratorwhere perhaps Jean-Luc has made his biggest impact on his students and colleagues.As a theoretical materials chemist, Jean-Luc has insisted that any theory or model be confronted with experiment, and that if the model fails then it must be improved.This directive has resulted in a dense web of synthetic, experimental, and theoretical collaborators and an uncanny ability to translate the varied languages of these scientific disciplines so that results of any work have the greatest impact.As can be seen by many of the papers that appear in this special issue, with reports that span a diverse array of the materials chemistry landscape, the tight feedback loop of the synthesis-theory-experiment triumvirate that Jean-Luc so strongly believes in permeates the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.037 |
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".