Bibliographic record
Abstract
Abstract Lake Erie is the southernmost, shallowest, warmest, most nutrient enriched, and biologically the most productive of the Great Lakes. The degradation of the Lake's water quality and the burning of the Cuyahoga River in the 1960s and 1970s made it the poster child for pollution problems in the world and paved the way for the Great Lakes Water Quality Agreement, the formation of USEPA, NOAA, and Environment and Climate Change Canada, the first Earth Day, and the passage of the Clean Water Act. A long history of overfishing led to the creation of the Great Lakes Fishery Commission and its highly successful interagency quota management system. The lake's extreme productivity and poor management allowed the zebra mussel and many other aquatic invasive species to enter and thrive. Good management in the 1970s and 1980s restored water quality and allowed the lake to become the Walleye Capital of the World. Today excessive nutrient loading has returned and many of the problems of the 1970s are back. The challenges are great and the solutions are different, but the lake is resilient and if we have the political resolve, it can be brought back again.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".