Review of: "Effective use of Waste Materials: A Case Study of Utilization of Fly Ash in Flexible Pavement Structures"
Bibliographic record
Abstract
In this paragraph "Classes of Fly Ash.Based upon the proportions of its chemical constituents, fly ash can be classified into class 'C' and class 'F'.Although researchers have not limited themselves to this classification……….",the authors should highlight the methods of classification of fly ash (ASTM C618-12 and Canadian Standards Association).-According to the ASTM C618-12, the classification is based on the chemical composition of fly ash.The major delimiter for this classification is the sum of silica, aluminium, and iron oxide percentages in the fly ash, being a minimum of 70% for a Class F and a minimum of 50% for a Class.According to the Canadian Standards Association, the classification of fly ash is based on the ratio of CaO, of which fly ash is generally low-calcium (Class F) when CaO is less than 10%.Please check and cite the previously recommended reference -Table 1 should include at least 5-10 different chemical compositions from different studies -In this sentence "while class 'F' is produced from lignite or sub-bituminous coal; the former class exhibits pozzolanic properties and the latter possesses cementitious properties [15].",please replace reference [15] by an ISI Web of Science reference
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".