Land use adjacent to wetlands in Southern Ontario
Bibliographic record
Abstract
Wetlands can be greatly affected by their adjacent land uses. Existing regional databases on land use and evaluated wetland location were analysed for the amount of various land uses abutting evaluated wetlands in southern Ontario. A sample of 27 National Topographic System map sheets was chosen based on a set of criteria to ensure representation of land use conditions. Wetland boundaries overlaid on land use maps were measured for the different abutting land uses. Evaluated wetland perimeters totalling 6,704.6 kilometers were measured within a sample study area of 21,445 square kilometers. Half the abutting uses were natural, dominated by forest. Agriculture accounted for 40% of the wetland perimeter - 11.4% Row Crop, 9.4%; Traditional Mixed and Grain systems; 18.2% Hay, Pasture and Grazing systems; 0.6% Specialty crop systems.Built-up uses abutted 5% of the evaluated wetland perimeter, dominated by rural road and non-farm residential uses. Water occurred along 4.4% of wetland perimeters. Minor differences are noted with a smaller sample set and between wetland class groupings. The spatial distribution of land uses abutting evaluated wetlands shows a wide range of wetland perimeter occurrence. Natural and agricultural uses dominate in 25 of the 27 sample areas. Agricultural system types adjacent to wetlands generally reflect their distribution in southern Ontario.
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| 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".