NRCS Regional Conservation Partnership Program - Great Lakes Region
Bibliographic record
Abstract
America’s Great Lakes — Superior, Michigan, Huron, Erie and Ontario — hold 21 percent of the world’s surface fresh water and host habitat for a variety of fish and wildlife species of concern. They provide drinking water for more than 40 million people and economic benefits from fishing and recreation. The Great Lakes Region is also a major agricultural area, with more than 55 million acres of land under production. All of these uses impact the Great Lakes ecosystem. With the CCA designation, USDA will build on existing strong partnerships in the Great Lakes Region to provide approaches and tools for producers to better manage nutrients and sediment on agricultural land. Accelerated conservation on private lands will help improve water quality, leading to better habitat for fish and wildlife and increased economic opportunities, including maintaining agricultural productivity in this vital region. This dataset includes a printer-friendly CCA map and shapefiles for GIS. Resources in this dataset: Resource Title: Great Lakes Region. File Name: Web Page, url: https://www.nrcs.usda.gov/programs-initiatives/rcpp-regional-conservation-partnership-program/critical-conservation-areas Information about the project and links to a printer-friendly CCA map (PDF, 1.2MB) and Shapefiles for GIS (ZIP, 232KB).
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.301 | 0.110 |
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".