Effects of Using Water Treated by Artificial Wetlands on Root Rot Suppression and Tomato Growth
Bibliographic record
Abstract
The objectives of this study were to evaluate the risks and benefits of using artificial wetland-treated waters to irrigate tomato plants ( Lycopersicom esculentum ) and the potential for suppression of Pythium ultimum . The experiment was conducted in a greenhouse using tap water (control) and treated waters coming from three types of horizontal subsurface flow artificial wetlands filled with pozzolana and implanted with common cattail ( Typha latifolia ). Wetland units contained either a simple [artificial wetland with sucrose (AWS)] or complex [artificial wetland with compost (AWC)] carbon source or no [artificial wetland with no carbon (AW)] additional carbon source. A complete randomized split-block design comparing root sensitivity to root rot (inoculated and uninoculated plants) in main plots and four nutrient solutions [1) control, 2) treated water from AWS, 3) treated water from AWC, and 4) treated water from AW] in subplots was used in six replications. Tomato plants were inoculated with P. ultimum twice during the experimental period. The use of treated waters reduced the in vivo root Pythium population by 84% and 100% when the treated waters were from AWS and AWC, respectively. In vitro trials showed that sterilization or membrane filtration (0.2 μm) of treated waters significantly reduced the potential for suppression of P. ultimum , suggesting that microbial activity played an important role. On the other hand, all AW-treated waters had a negative effect on root development of uninoculated young tomato plants. Root dry weights of plants irrigated with treated waters was 56% lower than in control plants, while their shoot:root ratio was two times higher for plants irrigated with treated waters. The inoculated and AWC-treated water treatments also reduced the Fv:Fm ratio of dark-adapted leaves, representing the maximum quantum efficiency of photosystem II. Organic compounds present in treated waters, expressed as total and dissolved organic compounds, may have affected tomato root development.
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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".