Assessing Land Loss from Flooding in the Lake St. Martin Basin in Manitoba, Canada
Bibliographic record
Abstract
Floodwaters from the Assiniboine River are typically diverted to Lake Manitoba and then Lake St. Martin to save the City of Winnipeg and Portage la Prairie from flooding. The four Indigenous communities living Lake St. Martin basin experienced many negative impacts, including long-term displacement after the 2011 flood. Data analysis of historical water levels of Lake Manitoba and Lake St. Martin were conducted to understand the impact of control structures and water levels. Satellite imagery allowed geographic information system (GIS) raster analysis of the shoreline change of Lake St. Martin before and after the 2011 super flood. From 1986 to 2010, the Lake St. Martin area increased slightly by approximately 0.63% but in 2011 the lake area increased by 13%. 11,000 acres were lost mainly around Lake St. Martin First Nation (LSMFN) reserve and the Little Saskatchewan First Nation, as a result and many houses and other buildings (churches, band offices, etc.) flooded in these communities. The shoreline change analysis showed that, in particular, the west and north shoreline of Lake St. Martin lost its beaches and lakefront properties, to become swampland after the 2011 flood. Thousands of Indigenous people were displaced for eight years, as the community could only start to rebuild its infrastructure and houses on higher ground after a government settlement was reached.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".