Networked lake science: how the Global Lake Ecological Observatory Network (GLEON) works to understand, predict, and communicate lake ecosystem response to global change
Bibliographic record
Abstract
The Global Lake Ecological Observatory Network (GLEON) has built an international, grassroots network of scientists and citizens, data, and lake observatories to advance understanding of lake ecosystems. Through careful attention to the professional needs and aspirations of a community, GLEON has formed as its foundation the trust and respect essential to product-based network science. As a consequence, GLEON is making significant advancements in lake ecosystem understanding through all “five legs of the table that support scientific understanding”—natural history, multiscale data, experiments, theory, and comparative studies—with particular emphasis on multiscale data and comparative studies. Technical products, such as cyberinfrastructure in support of network data and operations, software tools for calculating lake physical metrics (e.g., thermocline depth, buoyancy frequency, Schmidt stability), and lake metabolism, as well as ecosystem-scale numerical simulation software, have derived from GLEON collaborations and have become community resources catalyzing interdisciplinary science. Education and outreach initiatives have served to engage citizens from outside the traditional boundaries of academia directly in research. Moreover, these cross-boundary collaborations have provided essential links to lake and reservoir stakeholders who have informed how science is prioritized and communicated within GLEON. As a grassroots network, GLEON derives its momentum, flexibility, and impact from its talented members, who are committed to the future sustainability of lakes and reservoirs and the services they provide.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".