Treatments and Recycle of Greenhouse Solid Waste and Nutrient Feed Water
Bibliographic record
Abstract
Conservation of water through the reuse of wastewater is a concept prioritized worldwide. The recycling of greenhouse nutrient feed (GNF) water in greenhouse and nursery irrigation settings is no different as it allows growers to reduce uses of freshwater and fertilizer. The widespread recycling of GNF is challenged by the accumulated phytotoxic chemicals including metals and nutrient imbalances in the leached GNF along with presence of plant pathogens. In southern Ontario, disposal of GNF is regulated to protect Lake Erie from nutrient loads that result in eutrophication, a binational issue of USA and Canada. The producers also face an additional challenge of handling waste biomass consisting of diseased or dead plants, that can spread plant pathogenic microbes if not well-processed. Simple burning causes greenhouse gas emission. Research was completed to address these issues. \nThe processes investigated included the valorization of waste biomass into useful valuable solid and liquid products such as (1) hydrochar (HC) and activated carbon (AC), (2) chemicals such as costly 5HMF, acetic acid, and hydrocarbon fuel. Both HC and AC were utilized in GNF treatment achieving the desired recyclability and reuse of GNF. The efficiency of new products in treating GNF is comparable with conventional and advanced AC products. The HC was found to have a high heating value (about 26 MJ/kg), which can be used in greenhouse winter heating. Utilization of HC in pot soil remediation provided remarkable seed germination and plant growth. The liquid residue after extraction of valuable chemicals from process liquid was utilized as plants nutrient support with remarkable plant growth. The results show that recyclability of both the waste materials: GNF and waste biomass, is possible. These findings are beneficial to all stakeholders providing a sustainable and economic management technique of solid and liquid wastes
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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".