Organizational Structures and Data Use in Volunteer Monitoring Organizations (VMOs)
Bibliographic record
Abstract
Complex environmental problems call for unique solutions to monitoring efforts alongside developing a more environmentally literate citizenry. Community-based monitoring (CBM) through the use of volunteer monitoring organizations helps to provide a part of the solution, particularly when CBM groups work with research scientists or government managers. This study of volunteer monitoring organizations (VMOs) active in 2009 in the United States was conducted via survey in order to better understand the organizational structure, data collection procedures and data use of water-quality monitoring by volunteers, focusing on North Carolina. Organizational structures and origins of monitoring groups are discussed and reveal a wide variety of types and history of programs. Data collection procedures including required training and quality assurance were explored and discussed through the survey. Many groups require training of a varied type, but fewer complete quality assurance plans. Multiple types of volunteer monitoring data uses were indicated, including management and research. This study suggests a lack of structure at the state level may hinder the usefulness of data collected for purposes other than local information and environmental education. Cooperation between research scientists and VMOs may aid organizations in publishing more of their data and developing a quality assurance plan.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".