Analyzing Conflicts over Water Extraction from Great Lakes of North America through Game Theory Approaches
Bibliographic record
Abstract
Municipalities, water bottling companies, and other heavy water consumers are extracting large amounts of water from fresh water sources such as the Great Lakes, intensifying the already large effects imposed by extended periods of low rainfall and high temperatures. Applications for permissions to extract more water from the Great Lakes and also, the advantages and disadvantages of using different methods of water transportation have fueled a series of disputes. Great Lakes’ surrounding states and provinces try to protect the Great Lakes. But heavy water consumers seek further access to the rich water source. After identifying the important players, their preferences and options have been analyzed using the graph model of conflict resolution (GMCR) approach. It is suggested that the equilibrium state is a situation in which no water extraction permissions from the Great Lakes would be issued to additional parties. On the other hand though, a lot of water seekers would be trying to get approvals to access the Great Lakes. They would also however, seek other alternatives regarding their water requirements. This research should enhance understanding of the conflicts over water extraction and transportation from Great Lakes, and hence, help decision makers predict/prevent other potential water disputes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".