Electron spins from a molecular perspective: an interview with Song Gao
Bibliographic record
Abstract
ABSTRACT Spin chemistry has emerged as an interdisciplinary field that focuses on electron spins in molecules and related applications in physics, chemistry, biology, materials science and information science. It will play a crucial role in technological innovations. The chemistry of spins is deeply related to the essence of chemical bonds, chemical catalysis, enzyme catalysis, optical and electromagnetic properties, quantum computation and quantum precision measurements. The related research is intersectional, cutting-edge and has a wide range of application prospects. National Science Review (NSR) recently interviewed Prof. Song Gao to discuss spin chemistry. Prof. Gao is a renowned inorganic chemist and an academician of Chinese Academy of Sciences. He is the president of Sun Yat-sen University and the founder of the Spin-X Institute of South China University of Technology. He is recognized as a leader in coordination chemistry and molecular magnetism, and has strong academic influence. Since 2008, he has served on the International Advisory Committee of International Conference on Molecule-Based Magnets, and organized and chaired its 2010 conference. Prof. Gao's primary research topics include molecular nanomagnets; the relationship between geometric structures, electronic structures and magnetism of molecules; and the spin states and reactivity of molecules.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".