LWF Community Based Monitoring Program
Bibliographic record
Abstract
Across Canada, community-based monitoring networks are emerging as a means of engaging citizen scientists in collecting, analysing and sharing data about water quality and biological parameters.\r\n\r\nWithin Manitoba, many community and school groups have started water monitoring projects to engage students, landowners, cottagers, Indigenous nations and concerned lake-lovers. These citizen scientists are learning about the health of Manitoba\u2019s waters and engaging in solutions as they collect water samples across the province.\r\n\r\nThough active and enthusiastic, the Lake Winnipeg Foundation (LWF) observed that these groups were not currently co-ordinated within a larger network, and often did not have the ability to analyze their data and share information beyond their school or community. This is not for lack of interest \u2013 rather, local resources are limited and citizen scientists didn\u2019t have the opportunity to understand how their local data is part of a larger story taking shape throughout Manitoba.\r\n\r\nLWF is bringing these groups together to establish a strong community-based monitoring (CBM) network in Manitoba, supplied with standardised monitoring protocols developed by LWF\u2019s science advisers. This CBM network will:\r\n\r\n*Engage citizen scientists as champions for water health - particularly with respect to Lake Winnipeg, which is struggling with the negative effects of eutrophication;\r\n*Identify phosphorus hot spots on the landscape to ensure funding and action can be targeted to areas of greatest impact; and\r\n*Ensure a comprehensive, credible data set informs research and policy priorities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.127 | 0.022 |
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".