A Reconstruction of Wetland Information in Pre-Settlement Southern Manitoba Using a Geographic Information System
Bibliographic record
Abstract
Original Dominion Land Survey (DLS) township maps from the 19th century provide information to characterize pre-settlement surface conditions in the Canadian Prairies. Surveyors produced maps and detailed notes of landscape features such as wetlands, prairie, woodlands, water bodies and the locations of springs. Using these maps, stored in the Provincial Archives of Manitoba (PAM), historic wetlands and vegetation cover for a portion of the Red River basin in southern Manitoba were reconstructed and mapped with a Geographic Information System (GIS). To date, 100 townships, covering approximately 9,400 km2 have been captured in the GIS and analyzed into the categories of wetland, prairie, woodland, scrub and water. Wetlands represented 1,098 km2; prairie approximately 6,800 km2; woodland just over 1,000 km2; scrub about 500 km2; and water 3.2 km2. Applying these categories, a preliminary map of the pre-settlement landscape has been generated. The use of environmental reconstruction techniques, including written or graphic documentary evidence provides baseline information to improve understanding of environmental changes and causes of these changes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".