Agronomic Performance and Phytochemical Profile of Lettuce Grown in Anaerobic Dairy Digestate
Bibliographic record
Abstract
Anaerobic liquid dairy digestate is a by-product of dairy waste anaerobic digestion from dairy operations and is associated with environmental risks if not handled properly, particularly nutrient leaching losses, water contaminations, and greenhouse gas emissions. We tested the applications of anerobic digestate (AD) as a biofertilizer and water source in greenhouse vegetable production to integrate food production and industry waste management for sustainable environments. We used a deep water culture system to assess the effects of AD effluent alone, inorganic nutrient solution (NS), and a combination of AD and NS on the growth, yield, and phytonutrient profile and heavy metal contamination assessment of hydroponically produced lettuce. Lettuce produced in AD had a lower leaf area, total chlorophyll content, and fresh biomass; however, it displayed significantly higher chicoric acid (200%), chlorogenic acid (67%), luteolin (800%), quercetin-3-O-β-d-glucuronide (378%), quercetin-3-glucoside (200%), quercetin-3-O-(6″-O-malonyl)-β-D-glucoside (1077%), folate (248%), pantothenic acid (200%), total phenolics (111%), total antioxidants (44%), and soluble sugars (253%) compared to control (inorganic feed). The AD-produced lettuce also showed significantly lower heavy metal bioaccumulation risks associated with the human consumption. Based on various results, we may conclude that AD utilization in hydroponics can offer a sustainable solution to harvest a better lettuce yield, higher phytonutrients, and environmental benefits.
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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 | 0.000 | 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 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".