Bibliographic record
Abstract
Farming in Northern Ontario is limited to less than 1% of the total land area available. With over 2000 farms, this is home to about 6% of the province’s population, concentrated in the five major southern border cities of Thunder Bay, Sault Ste. Marie, Timmins, Sudbury and North Bay, with a significant presence of indigenous (i.e., First Nations) and disadvantaged peoples. This review highlights the challenges and opportunities of agriculture in Northern Ontario and offers a few strategies for establishing and sustaining agricultural operations locally. The challenges of farming in this region include the prevalence of adverse climatic conditions, lack of crop/economic diversification, insufficient infrastructure and support services, presence of small local markets, an aging population and youth out-migration, attitudes of dependency on government and limited investment potential. Nevertheless, this region offers much potential for farming as it contains significant amounts of fertile soils, good road networks and affordable land to start up farm businesses. Furthermore, the changing climate could be a boon to improve growing conditions, with expanded cropping options and increased yields in recent years. Production and consumption of local foods, conducting innovative on-farm research that addresses the needs of local producers including First Nations peoples, fostering regional research centres, building relationships through networking, exchange of ideas through effective use of different extension avenues, and collaboration and assisting local producers with market development may help establish a more competitive and sustainable agrifood sector in Northern Ontario. Favourable government policies to support growers who have experienced damage to their crops, forages and livestock due to adverse climatic conditions will further help sustain and expand their agricultural operations.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".