Hazard assessment for a pharmaceutical mixture detected in the upper Tennessee River using Daphnia magna
Bibliographic record
Abstract
Widespread use of pharmaceuticals has resulted in mixture concentrations ranging from mg/L in effluent to µg/L concentrations in surface water. In a 2008 study, 13 pharmaceuticals, ranging in amounts from 0.0028 to 0.1757 µg/l, were identified in the Tennessee River, USA and its tributaries. In order to address the need for risk assessment of environmentally relevant pharmaceutical mixtures, Daphnia magna 21-d life cycle tests were performed on a mixture of 11 of the 13 pharmaceuticals as well as on the individual components of the mixture. Mixture exposures were based on the same initial ratios of individual compounds, up to 1000x the initial mixture concentrations. The endpoints of mortality, time to first brood, size, and fecundity were the assessed. The LOEC of the 11- pharmaceutical mixture was determined to be 100x greater than the measured mixture concentration detected in the Tennessee River, with the NOEC being 75x that of the measured mixture. Single concentrations of pharmaceuticals within the mixture up to the 100x LOEC were not statistically different from control for any of the assessed endpoints. Thus, no single pharmaceutical was deemed predominately responsible for the mixture toxicity at the concentrations tested. While mixtures of pharmaceuticals are common in many systems, based on the findings of the present study, they may not pose a significant acute or chronic hazard to aquatic invertebrates at current concentrations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".