Aqueous Contaminant Removal and Stormwater Treatment Using Biochar
Bibliographic record
Abstract
Biochars are a class of charcoals made from sustainably sourced natural materials that are similar to activated carbons (ACs). They retain complex pore networks from their feedstock material and contain exceptional surface area that is created during production. The surfaces themselves are chemically complex and are responsible for the capacity of biochars to capture metal ions, pesticides, herbicides, and toxic organic molecules. Toxic organic molecules that have been successfully adsorbed by biochars include 2-, 3-, 4-, and 5-ring polycyclic aromatic hydrocarbons (PAHs); polychlorinated biphenyls (PCBs); pesticides and herbicides (atrazine, acetochlor, fipronil, pyrimethanil, etc.); sulfamethozole (antibiotic); and some explosives. Laboratory and field tests show that biochars can be integrated into filtration media used in stormwater best management practices (BMPs) for new construction and into retrofit applications that can improve current systems such as planted filter boxes, media filters, bioretention systems, green roofs, denitrification bioreactors, and sand filters. Because biochars are by-products of renewable energy systems and have the capacity to filter a wide array of emerging contaminants, they are exciting materials for environmental engineers and stormwater managers to improve water quality. Further research is necessary to verify the impact of biochars and biochar blends on stormwater filtration.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".