Effects of 25 pharmaceutical compounds to <i>Lemna gibba</i> using a seven-day static-renewal test
Bibliographic record
Abstract
Antibiotics are known to have antichloroplastic properties, but their effects on aquatic higher plants are virtually unknown. In order to address this issue, 25 pharmaceuticals, including 22 antibiotics, were assessed for phytotoxicity to the aquatic higher plant Lemna gibba. A 7-d static-renewal test was used, and plants were treated with 0, 10, 30, 100, 300, and 1,000 microg/L of pharmaceutical-containing growth media. Phytotoxicity was assessed using multiple growth and biochemical endpoints. Effective concentration (EC)50, EC25, and EC10 values as well as tests for significant differences between treatments and controls lowest-observed-effect concentration (LOECs) were calculated for each endpoint. Twelve different classes of antibiotics were assessed; however, only members of the fluoroquinolone, sulfonamide, and tetracycline classes of antibiotics displayed significant phytotoxicity. The most toxic members of each of these classes tested were lomefloxacin, sulfamethoxazole, and chlortetracycline, with wet weight EC25 values of 38, 37, and 114 microg/L, respectively. Injury symptoms were comparatively uniform and consistent among chemical classes while degree of phytotoxicity varied considerably. Both of these criteria varied markedly between classes. Wet mass was consistently the most sensitive endpoint above 100 microg/L; conversely, frond number was the most sensitive below 100 microg/L. Pigment endpoints were significantly less sensitive than growth endpoints.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".