Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.020 |
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 it