Toxicant interactions with food algae: A missing link between laboratory and field effects?
Bibliographic record
Abstract
Algae fed to invertebrate subjects of chronic toxicity testing are cultured without exposure to test substances. This approach may reduce the ability of bioassays to predict field effects because it assumes that bioconcentration is the only important uptake route, and that an interaction between toxicant and algae does not occur or is not relevant to the effect of the toxicant on test animals. The research presented in this paper focuses on the effects of a bleached kraft mill effluent (BKME) on algae used as food for test animals and the possible consequences of this exposure to bioassay results. The experiment consisted of exposing cultures of a pennate diatom, Navicula, to a range (0-7%) of BKME concentrations for 15 days. Final biomass (measured as chlorophyll a and ash free dry mass) was significantly greater in cultures exposed to 5% and 7% BKME. The carbon-to-nitrogen ratio was significantly higher in diatom cultures exposed to 7% BKME, and total lipid content ranged from 11.7% in the control to 15.8% in the 7% treatment. BKME exposure also increased bacterial content and altered the elemental composition (particularly strontium, barium, iron, and cobalt) of Navicula relative to control cultures. Because changes in food abundance and food quality (e.g., dietary lipids, carbohydrates, proteins) are known to modify toxicity and because contaminant uptake can occur through ingestion, exposing algal food supplies to toxicants would allow chronic bioassays to better simulate field conditions. This approach would be of value in situations where bioassays are intended to predict field effects rather than to compare the toxic potential of effluent samples. Although culturing food algae under exposure to contaminants poses methodological challenges, this approach may serve to enhance the predictive ability of chronic bioassays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 teacher head, 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".