Assessing the Potential Chronic, Lethal, and Multigenerational Ecotoxicity of Land-Applying Biosolids using Zea mays, Glycine max, Phaseolus vulgaris and Brassica rapa
Bibliographic record
Abstract
Abstract The uncertainty of potential toxicity when land-applying municipal biosolids to agricultural fields needs to be clarified considering the concomitant benefit for nutrient amendment and sustainability of resource recovery. This research is part of a larger program that assessed the toxicity of biosolids to terrestrial and aquatic organisms and this study specifically examined the toxicity of two biosolids when applied to four environmentally-relevant field crops. New bioassays were necessary to test the ecotoxicity of biosolids throughout the entire life cycle of each crop: Zea mays (corn), Glycine max (soybeans), Phaseolus vulgaris (common bean), and Brassica rapa (field mustard). It was hypothesized that biosolids would exhibit impact at both an environmentally-relevant application rate (8 tonnes ha-1) and a worst-case scenario (22 tonnes ha-1). The ecotoxicity of biosolids was tested using chronic, lethal, and multigenerational endpoints (i.e., F1 generation viability). Overall, study findings indicated a positive response to nutrient amendment using biosolids at either application rate. Negative responses to biosolids were seen in early growth stages of some cultivars (Zea mays) but disappeared or became positive as plants matured: these observations would have been made if existing protocols had been followed. Brassica rapa exhibited a negative germination rate when exposed to biosolids; however, further work is necessary to elucidate whether the effect is a result of nutrient additions or physical compaction on the small seed. The complete life-cycle bioassays of crops suggest that plants grown in the biosolids-amended soil were significantly larger and produced more seeds compared to reference assays. These results lend scientific support for their sustainable use in land-application strategies in Canada.
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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) | 0.000 | 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".