Bibliographic record
Abstract
The well known 36 distinguishable transformations between adjacent kaolin layers are split into 20 energetically distinguishable transformations (EDT) and 16 enantiomorphic transformations, hereafter denoted EDT*. For infinitesimal energy contribution of interactions between non-adjacent layers, the lowest-energy models must result from either (a) repeated application of an EDT or (b) alternate application of an EDT and its EDT*. All modeling, quantum input preparation and interpretation was performed with Materials Toolkit, and quantum optimizations with VASP. Kaolinite and dickite are the lowest-energy models at zero temperature and pressure, whereas nacrite and HP-dickite are the lowest-enthalpy models under moderate pressures based on a rough enthalpy/pressure graph built from numbers given in the supplementary tables. Minor temperature dependence of this calculated 0 K graph would explain the bulk of the current observations regarding synthesis, diagenesis and transformation of kaolin minerals. Other stackings that we list have energies so competitive that they might crystallize at ambient pressure. A homometric pair of energetically distinguishable ideal models, one of them for nacrite, is exposed. The printed experimental structure of nacrite correctly corresponds to the stable member of the pair. In our opinion, all recent literature measurements of the free energy of bulk kaolinite are too negative by approximately 15 kJ mol(-1) for some unknown reason.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".