Dataset for Mix Design and Performance of Low-Carbon Concrete Incorporating Multiple Waste Materials
Bibliographic record
Abstract
This dataset is provided as a Microsoft Excel file consisting of two worksheets, namely “Literature” and “Synthetic.” The Literature worksheet contains data compiled from previously published studies. In this sheet, Column A lists the corresponding references from which the data were extracted. Columns B to G present the mix design input parameters, including cement content (kg/m³), water-to-binder ratio (W/B), glass powder (GP, %), biomass fly ash (BFA, %), shredded rubber (SR, %), and superplasticizer (SP, %). Columns H to N report the concrete performance outputs, namely slump (cm), compressive strength (CS, MPa), bulk electrical resistivity (BER, kΩ·cm), rapid chloride penetration test results (RCPT, coulombs), global warming potential (GWP, kgCO₂/m³), air content (%), and splitting tensile strength (STS, MPa). When a reference does not report a specific input or output parameter, the corresponding cell is left empty; otherwise, the reported value is provided. The Synthetic worksheet includes the same set of input and output parameters; however, these data were synthetically generated using empirical equations to augment the dataset.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".