Bibliographic record
Abstract
Lakes Superior, Huron, Michigan, Ontario, and Erie together form North America's Great Lakes, a region that contains 20% of the world's fresh surface water and is home to roughly one quarter of the U.S. population (Figure 1). Supporting a $4 billion sport fishing industry, plus $16 billion annually in boating, 1.5 million U.S. jobs, and $62 billion in annual wages directly, the Great Lakes form the backbone of a regional economy that is vital to the United States as a whole (see http://www.miseagrant.umich.edu/downloads/economy/11‐708‐Great‐Lakes‐Jobs.pdf ). Yet the grandeur and importance of this freshwater resource are little understood, not only by people in the rest of the country but also by many in the region itself. To help address this lack of knowledge, the Centers for Ocean Sciences Education Excellence (COSEE) Great Lakes, supported by the U.S. National Science Foundation and the National Oceanic and Atmospheric Administration, developed literacy principles for the Great Lakes to serve as a guide for education of students and the public. These “Great Lakes Literacy Principles” represent an understanding of the Great Lakes' influences on society and society's influences on the Great Lakes.
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 | 0.003 | 0.007 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.011 |
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".