Construction of Functional Group Reactivity Database under Various Reaction Conditions Automatically Extracted from Reaction Database in a Synthesis Design System
Bibliographic record
Abstract
To be able to estimate the reactivity of functional groups under certain reaction conditions, we have stored three types of data: (1) data of change or destruction of the functional groups by the conditions of the reaction conditions; (2) data showing no influence of the reaction conditions on the functional groups; and (3) data showing the relative reactivity of two functional groups in the presence of certain reaction conditions. These three types of data, considered together, form entities that are referenced as "interaction data". These interaction data are used in a synthesis design system called SYNSUP. A new module in our system has been constructed that automatically generates interaction data from the reaction databases. From 15 265 reactions in the database, our program selected 2763 useful reactions with yields of > or =90% and one functional group change. From these useful reactions, data regarding 465 interferences, 815 cases of inert functional groups (under the reaction conditions), and 62 relative rate data could be extracted. In addition, with the use of multiple relative rate datasets, the reactivity of more than two functional groups could be deduced.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".