Recycled concrete, a solution for fine and coarse raw material for new concrete
Bibliographic record
Abstract
Solid waste generation of Hydraulic Concrete is a new polluting of the earth.The most produced material in the world is the Portland Cement (PC), but its disadvantage lies on its requiring fossil fuels, as well as the CO x discharge to the atmosphere.Re-use of Hydraulic Concrete waste abates simultaneously a number of problems, as dumping of solid waste and CO x compounds into the environment; affectation of quarry sand and gravel stones, as well as endemic flora and fauna living in them; storm-water runoff by the inability of strata to filter demolished concrete contained in landfills.The use of crushed aggregates coming from Hydraulic Concrete demolition is used to generate Recycled Concrete, a material that can abate costs, diminish pollution, reduce the cost of building, and protect quarries from unnecessary exploitation.Nonetheless, development of Recycled Concrete faces the challenge of finding optimal designs to achieve the highest mechanical performance under static and dynamic stresses, the need to produce concretes less susceptible to carbonation, and therefore, to corrosion of reinforcing steel that protect with the concrete core, aside from waterproof and dense concrete.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".