Single model establishing strength of dispersive clay treated with distinct binders
Bibliographic record
Abstract
Dispersive clays experience deflocculation in the presence of somewhat clean still water and are extremely vulnerable to erosion. Lime or Portland cement usage is one of the most applied methods to amend such adverse characteristics and enhance mechanical properties. Present research is aimed at a single power function quantifying the effect of amounts of binder, porosity, and curing period in the assessment of unconfined compressive strength (q u ) of dispersive clay–binder mixtures. Analysing q u results, it was found that a ratio between porosity and binder volumetric content controls the strength of blends. The q u values of the specimens moulded for each binder type were also normalized (i.e., divided by the q u attained at a specific porosity/binder ratio) reaching a single power function quantifying the influence of the binder’s amount, porosity, and curing time. From a pragmatic standpoint, this denotes that carrying out only one unconfined compression test with a specimen moulded with a specific binder, porosity, and cured for a given time period allows to determine an equation that controls the strength for a whole range of porosities and binder contents. The developed normalization was successfully extended to other fine-grained soils treated with cement, lime, and even pozzolan–lime, considering longer curing periods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".