Global Assesment of Interparticle Separation Distance on Low Cement Content Paste Mixtures
Bibliographic record
Abstract
A variety of research efforts on the concrete industry currently focus on the reduction of its most pollutant constituent, Portland cement (PC).Alternatives to reduce PC content in concrete such as the use of particle packing models (PPMs) and blended systems of PC and limestone fillers (LF) have been proven to be effective.However, the combined response of PPMs and LF at high levels of PC replacement still needs further investigation, particularly in the fresh state.In this work, the concept of Inter-particle Separation distance (IPS) is employed as a criterion to understand the behaviour of cementitious pastes made of PC and designed through continuous PPMs incorporating high LF dosages.Results suggest that IPS is a valuable tool to predict the fresh state behaviour of highly packed systems incorporating LF at low PC content.Moreover, the concept of interparticle separation among PC particles (IPScement) is proposed in this work to describe setting behaviour and compressive strength (f'c) of cementitious systems.iii Dedications: To the memory of my father: ‹‹Papá, you taught me the sacred value of family and the resilience to endure the toughest circumstances of life -Life is not always easy; it is on us to see the positive side of every experience because a simple smile can light up your day›› To my beloved wife and son: ‹‹Adriana, Ian: you have become my strength and joy of living.My love thanks for your patience
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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 | 0.000 | 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) | 0.001 | 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".