Collaboration: Water, A GLOBE Program Intensive Observation Period and Worldwide Cooperative Project
Bibliographic record
Abstract
Students K-16 in the United States and Canada joined their GLOBE Program peers from across the world in collecting water quality measurements during a week-long data-collection period in September, led by the GLOBE Africa Regional Coordination Office. The project was built off of other GLOBE collaborations around spring phenology measurements (Europe) and expeditions to Mt. Kilimanjaro and Lake Victoria (Africa). The efforts and resulting analysis of Collaboration: Water were supported by an international team of scientists, faculty and education professionals. The GLOBE Program Country Coordinators from the U.S. and Canada share the project goals, discuss the results of the September data challenge and how these lead into the community-based collaboration projects being developed between schools. Some of the projects will be presented during the International Virtual Science Symposium and Student Research Symposia in spring 2020. This project works on several levels. It creates resiliency locally through community-based inquiry, supports the development of 21st Century critical thinking, collaboration and communication skills and places the community investigations into the global context of the United Nations Sustainable Development Goal 6 (Clean Water and Sanitation). Along with tools, templates and the benefits of participation, the presenters will share how other communities can be involved in the March data collection event.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".