Isotope effects in liquid water by infrared spectroscopy. II. Factor analysis of the temperature effect on H2O and D2O
Bibliographic record
Abstract
Some 500 infrared (IR) spectra of light and heavy waters were obtained between 29 and 93 degrees C in order to identify the species present in liquid water. Factor analysis of these gives two species for each type of water with their IR spectra and abundance curves. Using an orthogonalization procedure, we obtained the temperature factor limits of -22 and +118 degrees C (+/-5 degrees C) that we coined cold and hot factors, respectively. Within experimental error, these limits are the same for light and heavy waters. The spectra of the orthogonalized factors presented show a decrease of the OH (OD) stretch band integrated intensities of almost 36% from the cold to the hot factors. No "free" OH (OD) group is present or formed in the temperature ramp. This indicates that all water molecules in the bulk are made of an oxygen atom surrounded with four hydrogen atoms, two covalently bonded, and two hydrogen bonded. This is consistent with the previous study of mixtures of H(2)O and D(2)O [part I: J.-J. Max and C. Chapados, J. Chem. Phys. 116, 4626 (2002)]. To maintain the ordinary liquid within the limits of 0 and 100 degrees C at atmospheric pressure, a fraction of the cold and hot factors are necessary. With the spectra of the cold and hot factors and the abundance curves, one can generate the spectrum at any temperature between -22 and +118 degrees C of light and heavy liquid waters.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".