Generalized Förster resonance energy transfer
Bibliographic record
Abstract
Introduction Decades of effort in structural biology and ultrafast spectroscopy have elucidated many details of natural photosynthesis (Blankenship, 2002; Hu et al. , 2002), and it is now a well-established fact that the initial light-harvesting process that leads up to the collection of energy at reaction centres, where charge transfer reaction occurs, is a process with almost perfect efficiency. The mechanism underlying the energy migration in a photosynthetic system is a fundamentally quantum-mechanical one, known as excitation energy transfer (EET) or resonance energy transfer (RET) (Silbey, 1976; Agranovich and Galanin, 1982; Scholes, 2003; May and Kühn, 2011; Olaya-Castro and Scholes, 2011). Resonance energy transfer is ubiquitous, and had been observed as sensitized luminescence long before modern quantum-mechanical understanding of molecular systems was established (Agranovich and Galanin, 1982). Normally, when a molecule becomes excited electronically by absorbing a photon, it luminesces by emitting another photon, within about a nanosecond, if it is fluorescence, or much later for phosphorescence. However, when another molecule with similar excitation energy is present within a distance of tens of nanometres, it can swap its excitation with the molecule as follows: D ∗ + A → D + A ∗ , where D ∗ ( D ) is the excited (ground) state donor of the energy and A ( A ∗) is the ground (excited) state acceptor. Thus, the excitation of D sensitizes that of A . Clear and sensible understanding of the RET process had been beyond the reach of classical mechanics as had any other molecular processes involving matter–radiation interaction.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".