Thermodynamic modelling: state of knowledge and challenges
Bibliographic record
Abstract
Over recent decades, understanding of the fundamental aspects of the chemistry of cement has advanced. Many of the leading-edge contributions on the subject were either authored by Professor Glasser and his colleagues or heavily inspired by their work. In the present paper, the limits and recent evolutions of thermodynamic models applied to cement systems are briefly presented, and their current limitations and future challenges are discussed. A number of examples illustrate how such models can be used to predict the influence of different factors such as cement composition, hydration, relative humidity or temperature on the composition and the properties of a hydrated cementitious system. The combination of chemical and transport models makes it possible to calculate the interactions of cementitious systems with the environment. However, precipitation and dissolution processes can be slow so that thermodynamic equilibrium may not always be reached, particularly during the first stages of the hydration process. This is why an approach that couples thermodynamics and kinetics could provide useful information. The introduction of kinetics would not only help understanding of the intricate influence of various factors, such as solution concentrations, on the hydration of cement but it would also provide the theoretical basis for the development of fully coupled thermo-poro-mechanical models for the prediction of the volume stability of cement systems exposed to chemically aggressive environments.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.014 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".