Sustainable solutions for soilless farming: reducing waste with vertical aeroponic systems
Bibliographic record
Abstract
The increase in global population has led to environmental challenges, particularly with increased food demand. Although traditional agriculture has served as a reliable practice for centuries, it has become increasingly unsustainable due to the overuse of natural resources. Under such circumstances, various farming companies have found new farming technologies that allow plant cultivation to occur without the use of soil to reduce land, water, and chemical usage. In Canada, Urban Lighthouse Farm Inc. (ULF) is currently the only vertical aeroponic farming company that is CanadaGap certified. ULF has identified several challenges within the food operation protocol including single-use materials and inefficient disposal of inconsumable plant waste, which could affect long-term sustainability. This study examined plant germination, growth, and reusability of unusable plant matter with four varying germination treatments. Food safe baskets (GyroCups™) and <em>Brassica</em> sp. plant seeds were used to determine a cost-effective and environmentally sustainable technique for plant germination. The fresh weight values of the <em>Brassica</em> sp. plants grown in the four germination methods varied. <em>Brassica</em> sp. plants grown with agar produced the highest weight values. The inconsumable roots from the harvested <em>Brassica</em> sp. plants were reused as a substrate for Blue Oyster (<em>P. columinous</em>) and Oyster (<em>P. ostreatus</em>) mushroom trials to determine the effectiveness of using agricultural waste as a substrate. The mushrooms with the highest recorded weight values were grown with the addition of sterilized roots. These findings provide ULF with a foundation for a “reduce, reuse, recycle” model to incorporate in their farming practices.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".