Land Suitability Assessments for Maize Production in Ontario, Canada, using a Weighted Overlay Method and Random Forest Algorithm
Bibliographic record
Abstract
This thesis aims to conduct land suitability assessments (LSAs) for maize production in Ontario, Canada for 2018 and 2080 under the Representative Concentrative Pathway (RCP) 4.5 scenario following the Food and Agriculture Organization of the United Nations (FAO) guidelines while considering climate, topography, and soil related factors. Two different approaches were used; namely a GIS-based weighted overlay technique, which is a conventional approach, and a novel statistical learning approach that employs the random forest (RF) algorithm with a new continuous measure of suitability introduced in this thesis. Both the conventional and statistical approach indicate that global warming will create more opportunities by 2080 for cultivating maize in Ontario with approximately 55% (546 000 km2) and 19% (183 000 km2) of Ontario’s land being suitable (highly and moderately suitable) for maize cultivation, respectively due to increasing growing season length, temperature, and precipitation. Regardless of the selected approach, there will be great economic significance involved with cultivating maize in Ontario by 2080 due to climate change while reducing food insecurity within Ontario and Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".