О ВЛИЯНИИ ДОБАВОК ПОВЕРХНОСТНО-АКТИВНЫХ ВЕЩЕСТВ ИЗ ОТХОДОВ ХИМИЧЕСКОГО ПРОИЗВОДСТВА НА ТРЕБУЕМЫЙ РАСХОД ВЯЖУЩЕГО ДЛЯ ПРИГОТОВЛЕНИЯ ОРГАНИЧЕСКИХ БЕТОНОВ
Bibliographic record
Abstract
In this work are studied and analyzed the methods of experimental and analytical methods of calculation of the consumption of astringent for the preparation of the classical crushed stone and crushed stone mastic asphalt concretes with using the siftings of crushed limestone for the upper layer of automobile road pavements. It is examined the infl uence for processes of pattern formation of additives of a small amount of stillage bottoms of local chemical industry, which are shown as surfactants in the oil bitumen and studying organic concretes. On the basis of the analysis of known theoretical concepts and studying of microstructures of samples of different composition is shown the possibility of reduction in the requirement in bitumen for making the classical crushed stone and crushed stone mastic asphalt concretes with the siftings of crushed limestone and is given the comparative assessment of the analytical methods of calculation of the consumption of bitumen for their preparation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".