Cold Climate Sustainable Urban Greenhouses
Bibliographic record
Abstract
Cities across the western world, especially those geographically situated in cold climates are struggling; they are trying to move toward urban sustainability, putting in place many programs and improvements to move in the right direction to decrease their carbon footprint and create long-term prosperity. One of the major improvements possible within the urban setting is to alter the way food is produced and consumed. Improving the agricultural system is the central theme of this research. Currently the western world operates on a mainly globalized food transportation system fuelled by fossil fuels. This creates potential food security, food sovereignty, and future urban sustainability difficulties. Therefore it is desired to move the agricultural system towards one which is mainly decentralized local growing. To do this the current greenhouses present in cold climates like Calgary need to be dramatically changed, improving energy efficiency. Three central issues were further examined: 1. Brownfield sites - Urban areas are difficult to produce food in economically due to high land prices; therefore utilizing brownfield sites is a solution to this problem. In them there is a large area of land within the urban setting attainable at a very low cost. 2. Greenhouse efficiency through design and technology improvements is necessary immediately if sustainability improvements are desired through moving to local agriculture. 3. Utilizing waste heat sources is important moving into the future. In this project the initial feasibility of using effluent waste water from the Bonnybrook WWTP in Calgary was examined. It was found through comparative analysis with other potential water sources to yield a financial gain, and a net energy gain. Further research is needed to make a full determination on the viability of the waste heat source.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".