Lake Winnipeg Basin Stewardship Fund - Map of Funded Projects
Bibliographic record
Abstract
The Government of Canada is committed to the long-term sustainability of Canada's lakes and waterways to ensure that there is clean water for all Canadians, both for this, and future, generations. To this end, on August 2nd, 2012, Prime Minister Stephen Harper announced the launch of Phase II of the Lake Winnipeg Basin Initiative (LWBI) with a five-year (2012-2017), $18 million investment through the Action Plan for Clean Water that will focus on improving water quality for people living in the region, as well as for fish and wildlife in and surrounding Lake Winnipeg. The Lake Winnipeg Basin Initiative aims to restore the ecological health of Lake Winnipeg, reduce pollution from sources such as agriculture, industry and wastewater, and improve water quality for fisheries and recreation. The Lake Winnipeg ecosystem supports an annual freshwater fishery of $50 million and a $110 million recreation and tourism industry. In addition, the Government of Canada is also providing support for community based projects through the Lake Winnipeg Basin Stewardship Fund - part of the Lake Winnipeg Basin Initiative and administered through Environment Canada's Lake Winnipeg Basin Office. The fund is cleaning up Lake Winnipeg by providing support to action-oriented water stewardship projects led by communities, conservation authorities, non-profit organizations and academic institutions. The following is a map describing the Lake Winnipeg Basin Stewardship Fund's funded projects at their geographical locations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.112 | 0.028 |
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".