Evaluating the Potential of Carbon Black for Safening of Herbicides in Soil
Bibliographic record
Abstract
Finely divided carbon char products offer a cost-effective method for the safening of herbicides in soil. In this investigation, two contrasting carbon char sources: Virgin Activated Charcoal (VAC) and Recovered Carbon Black (RCB) products were evaluated for safening of metsulfuron, sulfentrazone and pyroxasulfone herbicides in soil using sugar beet bioassay, and of rimsulfuron using perennial ryegrass bioassay. Plants were grown for seven days in WhirlPack® bags in soil with added carbon char product and added herbicide, and the root or shoot lengths were measured. The VAC was more efficacious in herbicide safening than the RCB. For the VAC, concentrations in soil (wt/wt) as low as 0.375% fully deactivated metsulfuron, sulfentrazone and pyroxasulfone, while for the RCB product a gradual degree of safening was observed, except for pyroxasulfone that was completely deactivated by both VAC and RCB. The VAC at concentrations of 3% and above fully deactivated rimsulfuron, while RCB did not. The two carbon char products examined in this study had different physical properties, of which the specific surface area (91 and 515 m2 g−1 for the RCB and VAC, respectively) and specific surface area of pores (75 and 247 m2 g−1 for RCB and VAC, respectively) showed the greatest difference. Higher surface area promoting greater sorption may explain more effective deactivation of herbicides by the VAC. To obtain the same degree of herbicide safening, more RCB would be required.
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.
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) | 0.001 | 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".