Bibliographic record
Abstract
The paper gives a comparative and historical assessment of climatic and economic hazards in the five main fruit‐growing areas of Canada, and how the industry has responded. The northern location of fruit growing in Canada poses a number of climatic hazards. The most serious hazard is winter low‐temperature injury to trees and vines, which results in a capital loss and crop loss for up to five years. The industry has responded to this hazard by abandoning fruit growing in areas with unfavourable climatic conditions and concentrating in areas with the most favourable climatic conditions. Other responses include changing to hardier varieties, improving management practices, and government subsidies. The fruit‐growing industry has also had to face many economic hazards, including changes in technology and consumer preferences, changes in international trade rules, increased foreign competition, a cost‐price squeeze, and urbanization of the industry's land base. These economic hazards area a greater threat to the industry than the natural hazards because, in most cases, there are limited coping mechanisms available to growers, and economic globalization is preventing the federal and provincial governments from providing an adequate safety net. L'article nous présente une évaluation comparative et historique des risques climatiques et économiques des cinq principales régions agricoles du Canada, ainsi que les réactions de cet industrie face à ceux‐ci. La situation géographique nordique des champs de fruits au Canada amène un certain nombre de risques climatiques. Les basses températures de la saison hivernale causent le plus de dommages aux arbres et aux vignes, ce qui se traduit en une perte de capitaux et une perte de récoltes allant jusqu'à cinq ans. La réaction de l'industrie de l'agriculture face à ces risques fût d'abandonner les endroits dont les conditions climatiques ne favorisaient pas la récolte de fruits. II a aussi été question d'utiliser des variétés plus résistantes au froid, d'améliorer les pratiques de management, et les subventions gouvernementales. L'industrie de la culture de fruits a aussi fait face à des risques économiques, incluant les changements technologiques et les préférences alimentaires des consommateurs, les changements au niveau des règles d'échanges internationaux, la compétition croissante au niveau international, une réduction du prix‐coûtant, et l'urbanisation des terres agricoles. Ces risques économiques représentent un plus grand danger pour l'industrie que les risques naturels puisque, dans la plupart des cas, les agriculteurs se retrouvent avec des moyens de rechange limités et la mondialisation économique empêche les gouvernements fédéral et provincial de fournir des mesures de sécurité adéquates.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".