Flipping Lakes: Explaining concepts of catchment‐scale water management through a serious game
Bibliographic record
Abstract
Abstract Ongoing anthropogenic and climatic pressures on inland waters have made water quality management a challenge of the 21st century. A holistic catchment‐scale approach to water management which includes stakeholder participation will be a key in maintaining lake health. A first step toward community engagement is to bolster environmental literacy on lake management, ecology, and eutrophication concepts of stakeholders now and in future generations. However, communicating with nonwater professionals about effects of pollution on water quality and catchment‐scale interactions across space and time can be difficult. Here, we present “Flipping Lakes,” a games‐based method for lake professionals to communicate and educate about catchment‐level water quality management to diverse audiences. In Flipping Lakes, the players take on the role of water managers in a catchment and are tasked to prevent a lake from “flipping” from a clear to a turbid state. During the game, the catchment slowly becomes polluted by a range of sources of which the effects are exacerbated by societal or climatic scenarios. Players need to implement measures while taking into consideration the intrinsic properties of the catchment in order to keep lakes clean. The game was tested with a diverse range of user groups and was well‐received. With its entertaining and accessible content, Flipping Lakes can lower communication barriers and increase understanding of difficult water quality concepts. The game is highly customizable, making it applicable to a variety of settings to support education and engagement of stakeholders and the broader community in order to address local water challenges around the globe.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".