Quantifying Urban Expansion and Prime Farmland Conversion in Southern Ontario through Multi-Decadal Remote Sensing Analysis
Bibliographic record
Abstract
In 2005 the Ontario Government implemented the Growth Plan to address growing concerns attributed to rapid urban development into prime agricultural areas. Quantifying prime agricultural land loss to urban development presents an opportunity to utilize remote sensing to track land use and land cover (LULC) change in the region. In Chapter 2, this research seeks to develop a methodology for assimilating previous LULC datasets alongside Landsat imagery and machine learning to create a 55-year dataset for the GTA. Chapter 3 utilizes the methods developed in Chapter 2 to quantify annual prime agricultural land loss with Canada Land Inventory soil quality data across all Southern Ontario from 1984 to 2023. The findings affirm trends found in contemporary literature of farmland loss occurring on prime agricultural soils to a much greater degree than non-prime soils. This study highlights the opportunities and challenges with a remote sensing approach to quantifying agricultural land loss.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".