Chlorophyll fluorescence as a bioindicator of effects on growth in aquatic macrophytes from mixtures of polycyclic aromatic hydrocarbons
Bibliographic record
Abstract
Chlorophyll-a fluorescence induction is a rapid technique for measuring photosynthetic electron transport in plants. To assess chlorophyll-a fluorescence as a bioindicator of effects of polycyclic aromatic hydrocarbon mixtures, chlorophyll-a fluorescence parameters and plant growth responses to exposure to the wood preservative creosote were examined in the aquatic plants Lemna gibba and Myriophyllum spicatum. Exposure to creosote inhibited growth of L. gibba (EC50 = 7.2 mg/L total polycyclic aromatic hydrocarbons) and M. spicatum (EC50 = 2.6 mg/L) despite differences in physiology. Creosote also diminished maximum PSII efficiency (Fv/Fm) (EC50 = 36 and 13 mg/L for L. gibba and M. spicatum) and the effective yield of photosystem II photochemistry (deltaF/Fm') (EC50 = 13 and 15 mg/L for L. gibba and M. spicatum). The similarity between growth and chlorophyll-a fluorescence EC50s and slopes of the response curves suggests a close mechanistic link between these end points. The predictive power of chlorophyll-a fluorescence as a bioindicator of whole-organism effects applied to complex contaminant mixtures is discussed.
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) | 0.000 | 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".