Restoration of Thunder Bay River Erosion Sites
Bibliographic record
Abstract
The Thunder Bay River is located in the northeastern corner of Michigan's Lower Peninsula. The river discharges into the Thunder Bay of Lake Huron at Alpena. The watershed area is approximately 1200 square miles. Soils in the watershed include sands, silts, clays, and organics. In 1993 the U.S. Department of Agriculture prepared a streambank erosion inventory of the Thunder Bay River. A total of 146 sites were identified. Causes of erosion included peaking hydropower operations, pedestrian foot traffic, cattle traffic, and stormwater drainage. As part of the new Federal Energy Regulatory Commission (FERC) license requirements, Thunder Bay Power Company (TBPC) is working with the U.S. Fish and Wildlife Service (USFWS), and associated partners to monitor and restore the more critical sites. Repairs have included structural measures such as riprap, bank sloping and bio-remediation. Monitoring is accomplished bi-annually using photography and field surveying techniques. The USFWS and TBPC are cooperating in grant applications for additional restoration work. TBPC has provided engineering and biological services for the projects. USFWS prepares the grant applications and monitors construction activities.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".