Bibliographic record
Abstract
The myriad of known factors that influence carbonation are divided into two (2) main categories consisting of the material system and environmental exposure. Under the materials category are compositional factors, physical parameters and serviceability factors, while the environmental category consists of the important exposure conditions especially RH, sheltering, atmospheric CO 2 concentration and temperature. The code-type engineering models employed in design are typically based on fundamental laws of physics and mathematical functions. This approach is also the methodology employed in development of the NCP model. This chapter presents an experimental justification of the model using worldwide data from independent research sources. The NCP model was validated using worldwide experimental data comprising natural carbonation of concretes in several cities and countries of Lyon (France), Austin (USA), Changsha (China), Chennai (India), Fredericton (Canada), along with data generated from urban locations of Pretoria, Durban and Johannesburg (South Africa). For each data set, the model’s predictions are compared with actual measured values of natural carbonation. Statistical error analysis is done to examine the model’s prediction accuracy. Employment of the data taken from worldwide sources covering the different human-inhabited global climates and geographical regions, also validates global applicability of the model.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".