Independent Versus Unified Management for the Great Lakes Basin
Bibliographic record
Abstract
"The five Great Lakes can be classified as a common property resource. This is a consequence of the lack of a well-defined system of property rights governing, water use in the lakes. Decisions by interested parties are interconnected, since withdrawing water from one point affects the water levels in the entire system. This, in turn, adversely affects hydropower production and commercial navigation. Contributing to the complexity of the problem are the eight U.S. states, two Canadian provinces and the two federal governments. Game theory will be implemented to describe this situation. There will be several games constructed to describe different market structures. Of particular interest is the number of players that participate in the game, as well as the expectations which they hold. Open-loop (where players commit themselves to future actions) and closed-loop (where players do not commit themselves to future actions) will be compared to the ten players game (eight states and two provinces), two players game (U.S. versus Canada) and one player game (a social planner's solution). It will be shown that trying to solve an open-loop game ignores part of the externalities involved, and thus can underestimate the social loss involved in these commons."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".