Aqueous Contaminant Removal and Stormwater Treatment Using Biochar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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 it