Adsorption of diclofenac onto different biochar microparticles: Dataset – Characterization and dosage of biochar
Bibliographic record
Abstract
Due to its wide occurrence in water resources and toxicity, pharmaceuticals and personal care products are becoming an emerging concern throughout the world. Application of residual/waste materials for water remediation can be a good strategy in waste management as well as in waste valorization. Herein, this dataset provides information on biochar application for the removal of emerging contaminant, diclofenac from water matrices. The data presented here is an extension of the research article explaining the mechanisms of adsorption diclofenac on biochars (Lonappan et al., 2017 [1]). This data article provides general information on the surface features of pine wood and pig manure biochar with the help of SEM and FTIR data. This dataset also provides information on XRD profiles of pine wood and pig manure biochars. In addition, different amounts of biochars were used to study the removal of a fixed concentration of diclofenac and the data is provided with this data set.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".