Exploring Urbanization-induced Wetland Loss Within the Greater Toronto Area From 2005 to 2015
Bibliographic record
Abstract
The Greater Toronto Area (GTA) located in Ontario, Canada, is one of the fastest-growing metropolitan areas in North America. Rapid urbanization within the GTA leads to increased imperviousness and surface runoff, resulting in wetland loss. Wetland cover and land cover data from the Southern Ontario Land Resource Information System was analyzed to characterize wetland loss to built-up areas and land conversions between 2005 and 2015 to evaluate the extent of urbanization-induced wetland loss. Spatial analysis determined a significant increase in the number of wetlands lost from 2005-2011 compared to 2011-2015; losses that were attributed to increased urban expansions within the GTA. Non-wetland conversions such as agricultural and impervious built-up uses to support urban expansions had a significant role in wetland loss. Wetland conservation policies must be re-evaluated to alleviate gaps in policy practice to focus on minimizing wetland 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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".