Reply to the ‘Comment on “Methanol dimer formation drastically enhances hydrogen abstraction from methanol by OH at low temperature”’ by D. Heard, R. Shannon, J. Gomez Martin, R. Caravan, M. Blitz, J. Plane, M. Antiñolo, M. Agundez, E. Jimenez, B. Ballesteros, A. Canosa, G. El Dib, J. Albaladejo and J. Cernicharo, <i>Phys. Chem. Chem. Phys.</i>, 2018, <b>20</b>, DOI: 10.1039/C7CP04561A
Bibliographic record
Abstract
In this Reply we answer the two main arguments raised in the Comment. The first argument is related to the binding energy of the methanol dimer and its influence on the dimerization rate constant. We show that the dimerization rate constants calculated in the Comment are unphysically low. We report values that are about two orders of magnitude higher than the values of the Comment, which confirm the conclusions of the original article that dimers can be present in a small amount. The second argument based on the dependence of the pseudo-first order rates on the methanol concentration was already explained in detail in the Supporting Information of the original article.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.058 | 0.058 |
| Insufficient payload (model declined to judge) | 0.008 | 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".