A lake management framework for global application: monitoring, restoring, and protecting lakes through community engagement
Bibliographic record
Abstract
Cianci-Gaskill JA, Klug JL, Merrell KC, et al. 2024. A lake management framework for global application: monitoring, restoring, and protecting lakes through community engagement. Lake Reserv Manage. XX:XXX–XXX.Despite decades of management and regulation, global freshwater resources remain imperiled. Management has had mixed success in restoring degraded lakes and has few mechanisms for stopping the decline of high-quality systems. Too often, lake managers play catch-up by addressing stressors only after damage occurs or has become entrenched, or make decisions without acquiring sufficient information about how a lake might respond to proposed management actions. As a tool to address these management challenges, we propose the MoReCo (Monitoring, Restoring/Protecting, Community Engagement) lake management framework. The framework centers around community engagement, and we outline engagement mechanisms in the context of lake management. The framework includes 2 loops: a monitoring loop to detect emerging stressors, and a restoring/protecting loop to address stressors that are causing or may cause lake degradation. The MoReCo framework builds on the strengths of existing natural resource management frameworks and was developed to address the unique challenges associated with lake management and protection, as well as those resulting from climate change. Specifically, it can address multiple stressors concurrently, which makes it simultaneously suitable for ameliorating stressors while also protecting lake ecosystems. The MoReCo framework is an interactive and multidirectional process in which management occurs even when no stressor is apparent, and it incorporates explicit benchmarks for evaluating management actions and determining whether additional measures should be taken. This novel lake management framework is suitable to address any stressors that may threaten a lake ecosystem, and we present it here as a resource for those who manage freshwater resources.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".