Bibliographic record
Abstract
Within this backdrop of public awareness and greater scientifi c evidence of the damaging eff ects of pollution to the water quality of the Great Lakes, a joint U.S.–Canada working group was formed to study the need for binational action to clean up the Great Lakes. Several years of study and two years of intense negotiation led to the signing of the Great Lakes Water Quality Agreement on April 15, 1972. In Canada, even prior to the signing of the Great Lakes Water Quality Agreement, a ban on phosphates in detergents was enacted as part of the 1970 Canada Water Act. Canadian scientists at that time were confi dent that phosphorus was the limiting (most crucial) nutrient contributing to accelerated eutrophication in the Great Lakes. Ontario through its implementation of a phosphate reduction and removal of phosphorus in its sewage treatment systems convinced the U.S. to follow the same course of action. Also, the Canada Water Act authorized the establishment of federal-provincial agreements to address water quality and resource management priorities leading to the negotiation of the fi rst Canada-Ontario Agreement (COA) which specifi cally designated the responsibilities of Ontario and the federal government regarding the Great Lakes Water Quality Agreement (Botts and Muldoon 2005).
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".