Quantification of the role of mineral fertilizer in the environmental performance of the Canadian cannabis (Cannabis Sativa) industry and urban soil-less farms
Bibliographic record
Abstract
This thesis presents a groundbreaking examination of environmental impact and sustainability practices in cannabis (Cannabis sativa) production and hydroponic agriculture through three unique experiments. Notably, it addresses a significant gap in research by quantifying the global warming potential (GWP) of outdoor cannabis production, an area previously unexplored despite its substantial presence in Canada and the United States.The first experiment assesses the environmental impact of outdoor cannabis cultivation, revealing insights into sustainable practices. Notably, it identifies the significant contribution of potting media to GWP and suggests strategies such as avoiding peat-based media and manipulating nitrogen deficiency to enhance cannabinoid production efficiency. Additionally, it introduces a novel functional unit (FU) of 100 mg of THC, potentially more suitable for extract and edible producers.In the second experiment, organic liquid fertilizer (OLF) is investigated in hydroponic systems using a unique bioreactor and basil as a model plant. This study highlights the potential for comparable plant growth with reduced greenhouse gas emissions, particularly when managing nitrogen loss during the bioreaction process. Utilizing locally-sourced biomass waste for nutrient cycling could substantially decrease the GWP of urban food production.The third experiment analyzes cannabis production in Canada, considering regional differences in climate, energy sources, and electrical grid carbon intensity. It concludes that outdoor production is environmentally friendlier, potentially reducing GWP up to 10-fold compared to indoor methods.Overall, these experiments contribute significantly to understanding environmental impacts and sustainable practices in cannabis and hydroponic agriculture. The findings offer valuable insights for growers, policymakers, and environmental advocates, guiding informed decision-making towards more sustainable agricultural practices. In an era of escalating climate change concerns, this research underscores the importance of sustainable agriculture in shaping the future of environmental stewardship
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".