Restoring Fish and Wildlife Habitat in U.S. Great Lakes Areas of Concern
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Beginning in 1985, remedial action plans were developed to restore any of 14 beneficial use impairments in Great Lakes areas of concern (AOCs). The designation of “loss of fish and wildlife habitat” as a beneficial use impairment helped elevate the priority for habitat restoration and helped focus AOC stakeholders on habitat restoration options and priorities. Funding from the Great Lakes Restoration Initiative (GLRI) has been the critical factor in realizing habitat restoration in U.S. AOCs, with over $280 million allocated since the beginning of the GLRI in 2011. Together, habitat restoration and contaminated sediment remediation have been a springboard for local communities to convert areas that were once a detriment to economic growth into valuable waterfront economic assets (e.g., Buffalo River AOC, River Raisin AOC, Sheboygan River AOC). These communities are transforming formerly polluted rivers in the Rust Belt into healthier and more attractive waterfront destinations for businesses, recreation, and tourism.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it