The Canadian Integrated Northern Greenhouse: A Hybrid Solution for Food Security
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it