Influence of Addition Contents of Iron Ore Tailings on Structural Mortar
Bibliographic record
Abstract
This study discusses and analyzes an alternative of IOT reuse as addition in structural mortars, evaluating the influence of IOT addition contents. The material was physically and chemically characterized. Structural mortars were molded with 10%, 20%, 30% and 40% of addition of IOT in relation to cement mass. We investigate physical properties, mechanical indicators, durability indicators and microstructure. The tailings are composed of predominantly quartz, hematite and goethite, and no components were found that could compromise the cementitious composites. The addition of IOT provided a tendency to reduce open porosity. The addition of 30% IOT was the most efficient in closing pores. All addition contents led to the maintenance or gains in mechanical properties, and a 10% gain in flexural tensile strength was observed for the addition of 10% IOT. The addition of 40% IOT promoted a 14% gain in compressive strength and maintenance of flexural tensile strength. This addition content evidences that the carbonation depth was not significantly affected. Based on the results, the addition percentages of 30-40% are interesting as to the analyzed properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".