Minimisation de la sensibilité à l'eau de composites cimentaires argile-schistes-bois
Bibliographic record
Abstract
The use of clay and wood waste in construction materials points out to a sensitivity to water, which is susceptible to reduce their durability. This work has first been interested in the influence of different constituents on the extreme dimensional variations (EDV) of such cementing composites. A formulation allowing the conciliation of environmental imperatives, interesting mechanical and thermal characteristics, and low density has been proposed. However, the EDVs remain above the required objective. In order to reach this value, different treatments have been examined. The addition of alkali-resistant fiberglass in the matrix did not lead to satisfactory results. Not only is the reduction of the EDVs insufficient for reasonable proportions of fibers, but this treatment also increases the proportion of absorbed water in presence of liquid water. Taken separately, both types of treatments used for wood particles - neutralization with hydraulic binders and extraction with boiling water of hydro-soluble compounds - also do not allow a sufficient reduction of EDVs. However, the combination of these two treatments allows the achievement of EDV [Formula: see text] 1 mm/m. Such a treatment improves the mechanical resistance without significantly altering the thermal performances.Key words: wood concrete, extreme dimensional variation, fiberglass, neutralization, hydrolysis.[Journal translation]
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".