Natural Analogues of Cement: Overview of the Unique Systems in Jordan
Bibliographic record
Abstract
In many radioactive waste repository designs, cement-based materials are expected to dominate the repository and models of cement evolution predict that leaching of the cementitious material in the repository by groundwater will produce an initial stage of hyperalkaline (pH∼13.3) leachates, dominated by alkali hydroxides, followed by a longer period of portlandite and C-S-H (CaO-SiO2-H2O) buffered (pH∼12.5) leachates. It has also been predicted that, as the hyperalkaline porewater leaches out of the near-field, significant interaction with the repository host rock and bentonite buffer and backfill may occur. This could possibly lead to deterioration of those features for which the host rock formation and bentonite were originally chosen (e.g. low groundwater flux, high radionuclide retardation capacity etc). The precise implications of cement leachate/repository host rock interaction has been studied in the laboratory and in underground research laboratories (URLs) and this work has been supported by study of natural cements in Jordan. These natural cements have been produced by the combustion of organic-rich clay biomicrites and are very close analogues of industrial cement. Following interaction with groundwaters, natural hyperalkaline leachates are produced and these move out of the cement into the surrounding host rock, subsequently interacting with and altering it.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".