Achievements and lessons learned from the 32-year old Canada-U.S. effort to restore Impaired Beneficial Uses in Great Lakes Areas of Concern
Bibliographic record
Abstract
Since 1985, governments and stakeholders have been developing and implementing remedial action plans to restore beneficial use impairments in polluted areas of the Great Lakes called Areas of Concern. Initially, progress was slow because of severity and geographic extent of the problems, lack of clarity on use of an ecosystem approach, time commitments for effective involvement of stakeholders, evolution of management programs, and need to secure restoration funding. Over time, many of these constraints have been overcome. Presently, as of 2017, seven Areas of Concern have been delisted, two have been designated as Areas of Concern in Recovery, and 18 have implemented all remedial actions deemed necessary for use restoration. Although progress has been made, much remains to be done to restore all impaired uses and delist all Areas of Concern. Notable achievements include: use of an ecosystem approach and requisite governance frameworks, contaminated sediment remediation, habitat rehabilitation, and control of eutrophication. Lessons learned are presented to help complete the cleanup of Areas of concern and help others restore degraded aquatic ecosystems worldwide.
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 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 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".