Urban sprawl into the Niagara Region: the effect of urban encroachment on agriculture seen through the use of remote sensing applications and landscape metrics
Bibliographic record
Abstract
The Niagara Region contains land that is ideal for agricultural practices. This thesis strives to illuminate whether or not urban growth in the Niagara Region is a detriment to agricultural land use. Using Landsat 5 TM and 8 OLI-TIRS satellite imagery, spatial statistics, called landscape metrics, will be utilized to determine growth and loss of urban and agriculture land uses. Satellite imagery will be classified based on researched methods in order to create land class maps. These maps will then be utilized for landscape metrics using the Patch Analyst extension for ArcMap. Change detection methods will also be observed. The above methods will be done for the overall landscape of the Niagara Region. This study will find that agriculture in the Niagara Region is changing and endeavors to highlight how urban sprawl is part of the cause. Fragmentation will be discussed as part of the issues due to urban sprawl.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".