Thermodynamic Analysis of the Two-Liquid Model for Anomalies of Water, HDL–LDL Fluctuations, and Liquid–Liquid Transition
Bibliographic record
Abstract
After reviewing the protocol-dependent properties of HDA, which thermally anneals to LDA, and the data gap over an unusually large T-range between HDA, LDA, and water, we investigate whether or not, despite HDA's ill-defined state and distinction from a glass, the HDL-LDL fluctuations view of the two-liquid model can explain water's anomalous behavior. An analysis of the density, ρ, compressibility, β, heat capacity, Cp, and thermal conductivity, κ, of water over a monotonic (continuous) path bridging this data gap shows the following: (i) Such a path between ρwater at 320 K and ρHDA yields an untenable thermal expansion coefficient of water. (ii) There is neither a continuous path between βwater at 353 K and βHDA, nor between Cp,water at 363 K and Cp,HDA. (iii) The same value of ρwater, of βwater, or of Cp,water at two temperatures separated by a maxima or a minima is incompatible with the HDL-LDL fluctuations view. (iv) κLDA at ∼140 K is about twice that of κ water at 253 K. (v) κHDA at 120 K is incompatible with κwater at T > 320 K. Thus, there is an internal inconsistency between the thermodynamics of HDA seen as a glass and that of water seen as an HDL-LDL mixture, which is incompatible with both the HDL-LDL fluctuations view and the liquid-liquid transition.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".