Experimental and Numerical Modeling of Arsenic Transport in Limestone
Bibliographic record
Abstract
Arsenic is a common constituent of the earth's crust. It is a potential carcinogenic element which presents in natural water systems as a result of both natural and anthropogenic activities. Arsenic poisoning is a burning issue for some parts of the world, and the complete removal of arsenic from water is still being studied. Arsenic is readily soluble and transports easily through groundwater, and, in addition to a human health problem, arsenic flow can create a vulnerable situation in the underground oil and gas reservoir. Some underground oil and gas reservoirs are made up of porous and permeable rocks. The properties of these rocks can cause the arsenic to be adsorbed when they come in contact. This research was focused on finding a limestone filter for purification of arsenic-rich water, which can also prove the possible arsenic effect on a limestone reservoir. Three types of limestone were considered to measure the propagation rate of arsenic through them. Results show that limestone can adsorb a significant amount of arsenic from aqueous streams and can be used as a potential filter for arsenic removal for a small community or for industrial purposes. Limestone can inhibit the release of arsenic water in nature. The predictive numerical model shows that oil and gas reservoirs are not in threat where there is abundant limestone available on the earth crust.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".