Use of a passive bioreactor to reduce water-borne plant pathogens, nitrate, and sulfate in greenhouse effluent
Bibliographic record
Abstract
The goal of this study was to evaluate the use of passive bioreactors to reduce water-borne plant pathogens (Pythium ultimum and Fusarium oxysporum) and nutrient load (NO(-) 3 and SO(2-) 4) in greenhouse effluent. Sterilized and unsterilized passive bioreactors filled with a reactive mixture of organic carbon material were used in three replicates. After a startup period of 2 (sterilized) or 5 (unsterilized) weeks, the bioreactor units received for 14 weeks a reconstituted commercial greenhouse effluent composed of 500 mg L(-1) SO(2-) 4 and 300 mg L(-1) NO(-) 3 and were inoculated three times with P. ultimum and F. oxysporum (10(6) CFU mL(-1)). Efficacy in removing water-borne plant pathogens and nitrate reached 99.9% for both the sterilized and unsterilized bioreactors. However, efficacy in reducing the SO(2-) 4 load sharply decreased from 89% to 29% after 2 weeks of NO(-) 3-supply treatment for the unsterilized bioreactors. Although SO(2-) 4 removal efficacy for the sterilized bioreactors did not recover after 4 weeks of NO(-) 3-supply treatment, the unsterilized bioreactor nearly reached a similar level of SO(2-) 4 removal after 4 weeks of NO(-) 3-supply treatment compared with affluent loaded only with SO(2-) 4, where no competition for the carbohydrate source occurred between the denitrification process and sulfate-reducing bacteria activity. Performance differences between the sterilized and unsterilized bioreactors clearly show the predominant importance of sulfate-reducing bacteria. Consequently, when sulfate-reducing bacteria reach their optimal activity, passive bioreactors may constitute a cheap, low-maintenance method of treating greenhouse effluent to recycle wastewater and eliminate nutrient runoff, which has important environmental impacts.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".