Bibliographic record
Abstract
The increased usage of pharmaceuticals, especially antibiotics has unintentionally resulted in their increased introduction into the environment, especially via effluent into waterways from wastewater treatment plants. Many of these drugs can have significant detrimental effects on local wildlife and flora and have the potential to be introduced into the food chain. Sorption of these trace pharmaceuticals to environmental surfaces is of interest as it plays a key role in their mobility and remediation. In this study the detection, characterisation and quantification of the sorption of Ciprofloxacin (cipro) to goethite, a iron oxide mineral commonly found in soils, and biochar. The sorption was tested from pH 3-10 and was analyzed using HPLC-MS and FTIR spectroscopy. Sorption of the cipro was confirmed and 3 potential breakdown products were discovered. These breakdown products are likely produced by oxidation at the mineral surface. Sorption efficiency was determined using HPLC-MS, and found to be most efficient from PH 6-8. The results of this preliminary study will be used in future works in predicting the mobility of trace pharmaceuticals and their breakdown products in environment. Department: Physical Sciences Faculty Mentor: Dr. Janice Kenney
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".