Bibliographic record
Abstract
Yasuhiro Furuichi, who made momentous contributions to our understanding of eukaryotic mRNA biogenesis and function, passed away in his home in Kamakura on October 8, 2022, after a long battle with pancreatic cancer.Yasuhiro Furuichi was a towering scientist and imaginative researcher.His ideas and accomplishments left a lasting impact on our knowledge of the basic mechanisms of gene expression.We were honored to share with him over the years his excitement about science, and we are grateful for the privilege of having had the opportunity to work with him.We were inspired by his enthusiasm, collegiality, and friendship and are thankful for his mentoring.Yasuhiro (Hiro, as we all referred to him) is credited with the discovery of mRNA capping and its role in mRNA stability and function.However, as described below, Hiro's contributions extend to many other areas of molecular biology, covering both fundamental aspects as well as the conversion of basic knowledge into theories of human disease and prevention.For both of us, our friendship and scientific adventure with Hiro started in the mid-1970s, when the three of us worked on mRNA capping in the laboratory of Aaron Shatkin at the Roche Institute of Molecular Biology (RIMB) in Nutley, New Jersey.Hiro got there first, arriving from Japan in June 1974 after publishing-while working in the laboratory of Dr. Kin-Ichiro Miura at the National Institute of Genetics in Mishima-a highly surprising ob-
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.014 |
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".