Bibliographic record
Abstract
The Great Lakes are vast. The five lakes that make up the system—Superior, Michigan, Huron, Erie, and Ontario—comprise the largest freshwater system on Earth and contain approximately onefifth of the world’s water supply.1 The Great Lakes provide water for consumption, highways for trade and transportation, fuel for power, and natural beauty for recreation.2 Approximately 35 million people live within the Great Lakes Basin, and 23 million depend on the Lakes for their drinking water.3 The Lakes are more than 750 miles wide and have a surface area greater than 300,000 square miles; there are 25,000 square miles of connected smaller lakes, hundreds of miles of navigable rivers, and 10,000 miles of shoreline.4 Simply put, the Great Lakes are enormous in their physical size and quantity of water. The enormity of the Great Lakes is matched by a governance and legal regime that can overwhelm attorneys and policymakers. The system is shared and governed by two countries, eight states,5 two provinces, and numerous Indian tribes and First Nations, in addition to a multitude of American, Canadian, and international agencies, as well as thousands of local governments.6 This “patchwork” of Great
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".