Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A predictive compressive strength model accounting for the type of cement, cement degree of hydration, aggregates type and gradation, mixtures proportion and air content was developed. This paper presents the formulation, implementation, calibration and validation of the proposed strength model for normal concrete. The theoretical formulation postulates that particles' interaction is governed by excess paste theory from which an average paste thickness model is developed to account for concrete mixture proportions and aggregate gradation. In addition, the model accounts for the cement compressive strength and aggregate to cement paste bond strength. An experimental programme, developed to evaluate the model, accounts for the following variables: water to cement ratio, water content, bulk volume and maximum size of coarse aggregate, and air content. The proposed model is found to accurately predict the strength of concrete mixtures at 3, 7, 28 and 191 days. The measured 3-day and 28-day strength range from 8·5 to 32·7 MPa and from 13·6 to 43·8 MPa, respectively. The corresponding standard error and correlation coefficient for the 3-day predictions are 2·1 MPa and 0·95, and 1·8 MPa and 0·96 for the 28-day predictions.
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.
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 | 0.001 | 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 | 0.001 | 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 it