Science as a fundamental framework for shaping policy discussions regarding the use of groundwater in the State of Michigan: a case study
Bibliographic record
Abstract
The availability and use of freshwater is a growing concern in the United States and around the globe. Despite apparently abundant water resources, several conflicts over water use have emerged in the Great Lakes region and the State of Michigan. These conflicts resulted in state legislation that both addresses water withdrawal from the Great Lakes Basin and requires the State of Michigan to begin a process to address the sustainability of water resources. The former resulted in Michigan's support of the Great Lakes-St. Lawrence River Water Resources Compact, whereas the latter resulted in the formation of a Groundwater Conservation Advisory Council. This paper focuses primarily on the Council, describing its formation, and the products it generated. In particular, we focus on the development of indicators of sustainable use of water, the creation of a water withdrawal assessment process to determine if a proposed withdrawal will create an adverse resource impact in the state, and how the lessons learned in Michigan may be applied to other units of government addressing similar issues. Attention is also given to the Compact, as it provides important context for the Council's formation.
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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".