Interactive Web-Based Visualization for Lake Monitoring in Community-Based Participatory Research: A Pilot Study Using a Commercial Vessel to Monitor Lake Nipissing
Bibliographic record
Abstract
Environmental and limnological monitoring is of interest to government agencies, researchers, and the general public. In communities that rely on and are heavily affected by lakes and their watersheds, accessible and intuitive presentation of lake properties influences and aids decision-making, interventions, and the formulation of environmentally sound policies. In this paper, interactive web-based visualizations are employed as a mechanism to communicate environmental information collected from a commercial cruise vessel. A pilot study is presented for monitoring Lake Nipissing, a large culturally and environmentally important lake in northeastern Ontario, Canada. This example of community-based participatory research suggests that: (1) policy makers and researchers can quickly gain insight into what is happening in the lake through visualizations, which helps to direct subsequent, detailed investigations; and (2) through accessible, visual presentation, community members may be encouraged to become involved in contributing to environmental policies that directly affect them, thereby supporting environmental “citizen science”.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".