High-frequency lake data benefit society through broader engagement with stakeholders: a synthesis of GLEON data use survey and membe rexperiences
Bibliographic record
Abstract
The Global Lake Ecological Observatory Network (GLEON) has a tremendous opportunity to facilitate greater public understanding of lakes and enable evidence-based decision making for freshwater ecosystems with high frequency data. To investigate this potential as well as the scope of outreach activities currently underway, we surveyed the 46 GLEON sites active as of 2013 about the uses of the high-frequency lake data (HFD). Of the 26 who responded, 69% engaged in or were aware of the use of GLEON HFD beyond academics. To highlight some of the outreach activities conducted in collaboration with GLEON scientists, we elaborate on 3 categories of data use: (1) engaging with citizens, (2) educating students and teachers, and (3) aiding in decision making. When synthesized with a discussion of examples of broader engagement activities across the network from the perspective of participants, the results suggest GLEON’s network science approach enables the diffusion of ideas and tools for conducting effective outreach. Results also point to opportunities for GLEON to build on existing experience to encourage greater engagement of member scientists in lake conservation, restoration, and management. In light of the growing challenges in managing water quality and quantity, our findings will help determine best practices and provide guidance to scientists on how to engage a broader range of stakeholders in lake research and management.
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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 | 0.032 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".