Habitat rehabilitation in the Detroit River Area of Concern
Bibliographic record
Abstract
Loss and degradation of fish and wildlife habitat is a long-standing issue in the Detroit River. The Detroit River Remedial Action Plan helped agencies and stakeholder groups reach agreement on impaired beneficial uses, including loss of fish and wildlife habitat, and helped mobilize all stakeholders to rehabilitate habitat. Many organizations played key roles, including the Detroit River Public Advisory Council, Detroit River Canadian Cleanup, State of the Strait Conferences, American and Canadian Heritage River Initiatives, Detroit River International Wildlife Refuge, and Western Lake Erie Watersheds Priority Natural Area. Accomplishments include: 14 habitat restoration projects on both the Canadian and U.S. side of the Detroit River; 53 soft shoreline engineering projects in the watershed; nine fish spawning reefs in the river, Common Tern habitat in four locations; and many wetland and green infrastructure projects. Based on Detroit River habitat restoration efforts over the last 32 years, the following advice is offered: reach agreement on severity and geographic extent of the problem; practice adaptive management; involve habitat experts up front in project design; establish quantitative targets for project success; ensure sound multidisciplinary technical support; start with demonstration projects; treat habitat projects as experiments; involve citizen scientists in monitoring; measure benefits; communicate and celebrate successes; promote education and outreach.
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 | 0.000 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".