Advances in the study of supercooled water
Bibliographic record
Abstract
Agradecimentos: V.F.-L., J.B., C.M.T., C.G., R.B., and T.L. gratefully acknowledge financial support by the Austrian Science Fund (FWF, project I1392) and Deutsche Forschungsgemeinschaft (DFG, grant no. BO1301/12-1 and grant no. BO1301/15-1). J.B, V.F.-L., and C.M.T. are recipients of a DOC fellowship of the Austrian Academy of Sciences. L.E.C. and G.F. acknowledge support by Spanish grant PGC2018-099277-B-C22 (MCIN/AEI/10.13039/501100011033/ERDF "A way to make Europe"). L.E.C. acknowledges support by grant no. 5757200 (APIF_18_19 Universitat de Barcelona). G.F. acknowledges support by ICREA Foundation (ICREA Academia prize). I.d.A.R. and M.d.K. acknowledge support from CNPq, Fapesp grant 2016/23891-6 and the Center for Computing in Engineering & Sciences-Fapesp/Cepid no. 2013/08293-7. IdAR and MdK acknowledge the National Laboratory for Scientific Computing (LNCC/MCTI, Brazil) for providing HPC resources of the SDumont supercomputer. URL: http://sdumont.lncc.br. J.M.M.d.O., F.S., and G.A.A. acknowledge support from CONICET, UNS, and ANPCyT (PICT2015/1893 and PICT2017/3127). P.H.P. thanks NSERC Canada, ACENET, and Compute Canada for support. G.A.A. and H.R.C. acknowledge support from CONICET, UBA, and CNEA for organizing the 3rd International Workshop "Structure and Dynamics of Glassy, Supercooled and Nanoconfined Fluids," Buenos Aires (Argentina), July 2019, which was the germ of this review work
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".