The Canadian Integrated Northern Greenhouse: A Hybrid Solution for Food Security
Bibliographic record
Abstract
Food security has become a prominent issue in northern Canada. Many constraints, including environmental, cultural and economic barriers to cause food insecurity in northern Canada where local food production is one proposed solution to the northern food crisis. Initiated at McGill University by the Biomass Production Laboratory, the Canadian Integrative Northern Greenhouse (CING) unit provides a completely integrative design solution that could allow northern Canadian communities to grow their own fresh and nutritious food year-round. The CING unit is a hybrid between a northern greenhouse and a growth chamber housed in a shipping container, designed to be adaptive by functioning as a typical solar greenhouse when solar light provides considerable heat and light, and as a closed growth chamber during the night and when colder, darker winter conditions prevail. The CING was designed and prototyped by McGill students since 2013. Lettuce was grown during the four-season test of the CING, the greatest yield obtained was in March 2019, where the plants grown achieved 72% of the dry mass of the plants grown in the research greenhouse. The CING relied on supplemental heating to successfully grow plants but demonstrated the potential for northern and remote applications.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".