Bibliographic record
Abstract
Plants and photosynthetic bacteria obtain their energy from sunlight or surrounding radiation. Their photosynthetic membranes are composed of a much elaborated series of antenna molecules based on chlorophylls or bacteriochlorophylls, carotenoids playing multiple roles, various electron transport accessories, and central special pairs. The latter components are the most difficult to mimic with exactitude because the structure−property relationship depends on many factors including interplanar distance, slip angle, substituents, metal, and axial ligand. To this list of factors to control with quasi-perfection, one should also add the thermal activation (i.e., temperature). Over the past 15 years or so (2001–2013), an intensive collaboration with Professor Roger Guilard (Université de Bourgogne, Dijon) dealt with elucidating the role of each parameter to provide the best design of artificial special pairs capable of responding or behaving like the natural special pairs, namely with regards with the antenna effect. The latest feature is one of the defence mechanisms slowing down the rate for the primary electron transfer from the special pair to the electron transport accessories. This review highlights the advances in this challenging area of mimicry of the photophysical events in biological systems, namely the artificial special pairs designed in our laboratory for the antenna processes.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".