Lake Champlain Community Scientist Volunteer Network Communicates Critical Cyanobacteria Information to Region‐wide Stakeholders
Bibliographic record
Abstract
Abstract Lake Champlain is a treasured resource for recreation, tourism, and drinking water situated in New York, Vermont (U.S.), and Québec (Canada). Because its shores span two states and two countries, management strategies for the lake require strong cross‐boundary partnerships and cooperation. In recent decades, increased prevalence of harmful cyanobacteria blooms has impacted public health and recreation. A lake‐wide cyanobacteria monitoring program was established in 2001 with an emphasis on water sample collection and analysis to inform management strategies. In 2012, this program transitioned from laboratory‐based analyses at a limited number of locations to a visual assessment protocol validated by water samples. This transition opened the door to more effective and widespread monitoring, communication, and inclusion of a greater number of monitoring locations and stakeholders. Today, through a unique partnership of community scientist volunteers, public beach managers, nonprofit organizations, and state and federal agencies, a comprehensive network of trained cyanobacteria monitors generates timely data on water quality conditions to relay critical public health information. The majority of these reports are provided by trained community scientist volunteers, strengthening the geographic coverage of the program and the environmental literacy of lake users. This program now trains hundreds of community scientists, documents thousands of water quality condition reports annually, and communicates cyanobacteria conditions to the public via an online Cyanobacteria Tracker map. In this article, we describe the evolution of this successful program, discuss key findings from analysis of these volunteer‐collected data, and suggest how similar programs could be effectively developed in other regions.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".