PYRITE, MARCASITE, AND PYRRHOTITE: POTENTIAL CONSTRUCTION HAZARDS IN NEW ENGLAND AND BEYOND
Bibliographic record
Abstract
Iron sulfides such as pyrite, marcasite, and, most notably as of late, pyrrhotite are known to cause concrete failure. These iron sulfide minerals cause damage either by incorporation into concrete mixes, or by merely being present in the surrounding native soils or fill in which the structural foundation is built. Both processes are highly dependent on oxygen, iron sulfide, and carbonate availability, however the oxidation reaction of iron sulfide minerals is cyclic and will continue once oxygen has been introduced to the system. Countries such as Canada, Japan, Namibia, and Ireland struggled with structural failures due to iron sulfide in the past. Regulations as to the amount of acceptable iron sulfides vary by country and there is no universal standard. For example, Ireland has a maximum allowable sulfate of 0.2% in an acid soluble test, but this test does not measure the amount of pyrite, only the soluble sulfate, which is a byproduct of pyrite oxidation. The European standard EN 12620 requires total sulfur content to be less than 0.1% if pyrrhotite is detected, or 1% if only other iron sulfides are present. Canada and Japan have also determined acceptable amounts of sulfate or total sulfur allowed in concrete. While the US uses the International Building Code thresholds for all soluble sulfates in contact with concrete, a national standard for pyrite or related iron sulfide minerals in building materials, fill, or the native soils has not been created for the United States. Increasingly, iron sulfide minerals have been identified as the causes of concrete degradation in homes and other structures in the northeast US, leading to interest in hazard detection and mitigation. Identification of iron sulfide risk through a combination of visual, geochemical, and geophysical methods is most effective in preempting iron sulfide-based damage, and communication of the potential hazards to the public can lead to more informed decisions regarding construction materials and locations.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".