Bibliographic record
Abstract
I often say that for those of us who live in Michigan, the Great Lakes are part of our DNA. I have no doubt the same applies for the millions of people living in the seven other states in the Great Lakes basin and for our friends in Canada.The Great Lakes are home to 107 million people, 3,500 unique species of plants and animals, world-class beaches and fisheries, and a $6 trillion economy that supports 50 million jobs. They also represent the largest surface freshwater system on Earth, supplying drinking water to over 40 million people. The economic, environmental, and ecological significance of our lakes simply cannot be overstated.We cannot take this precious resource for granted. Sadly, the Great Lakes continue to face a multitude of environmental and ecological threats, including harmful toxic pollutants and invasive species. That is why in 2010, as a member of the Senate Budget Committee, I authored the Great Lakes Restoration Initiative - the first and largest funding stream dedicated to protecting the Great Lakes. Through bipartisan partnerships in Congress, in collaboration with state and local regulators, international and regional entities, and the scientific community, we must remain dedicated and focused on solutions that address the complex challenges facing our waters.Thank you to all of the contributors to this special issue of the journal of the Aquatic Ecosystem Health and Management Society. The sound scientific assessments provided here offer important lessons for every person who is committed to protecting and restoring this critically important aquatic ecosystem. Working together, we will continue to tackle these challenges head-on and protect our Great Lakes for future generations.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.487 | 0.505 |
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".