Canadian Greenhouse Operations and Their Potential to Enhance Domestic Food Security
Bibliographic record
Abstract
Food security is a growing societal challenge. The pressure to feed a projected global population of 9.6 billion by 2050 will continue to be limited by decreasing arable land. The recent disruptions in international trade resulting from responses to the COVID-19 pandemic have highlighted the importance of regional self-reliance in food production. While Canada is highly self-reliant in food categories such as meat and dairy, the nation relies heavily on international imports to fulfill fresh vegetable demands. In potential future scenarios where international trade faces disruptions, Canadian food security could be at risk. By providing local sources of fresh foods year-round, the greenhouse vegetable industry holds strong potential to overcome future food supply shortages and could become a critical contributor to self-sustainable food production in Canada. Many challenges, however, surround the Canadian greenhouse industry. Some challenges include the persistence and spread of infectious plant pathogens and forecasted labour shortages. Opportunities to alleviate such challenges include introducing more diverse commodity groups and integrating innovative technologies to accelerate efficiency within the industry. In this commentary, we examine the current state of the Canadian greenhouse industry, explore potential challenges, and highlight opportunities that could promote food security across the nation.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".