Critical intersections of flow: Connecting waters among the Laurentian Great Lakes
Bibliographic record
Abstract
An analysis of the state of water quality and ecosystem services in the rivers (St. Marys, St. Clair, Detroit, Niagara, St. Lawrence), fluvial lake (St. Clair), and strait (Mackinac) that connect and drain the Laurentian Great Lakes was conducted by the Great Lakes Science Advisory Board of the International Joint Commission (Canada, United States of America). Although these boundary waters are defined under the Great Lakes Water Quality Agreement and are associated with lakewide management plans, they have historically received inadequate attention regarding surveillance and monitoring. As a result, the data and knowledge bases for connecting waters are far less complete than for the open water and nearshore regions of the lakes, given the intensity of threats and the ecosystem services that intersect in connecting waters. This commentary reviews and discusses the current status of monitoring infrastructure and activities in the Great Lakes connecting waters. Several specific recommendations are made to support development of integrated connecting water research and practice: the development of highly qualified personnel trained to conduct research on large moving waters; establishment of well-equipped and staffed research vessels with appropriate sampling gear; support for shore-based university and agency laboratories to focus on a mix of long-term reference stations and experimental studies; expanded use of real-time monitoring systems using evolving technologies; and increase in Indigenous community technical capacity for environmental monitoring and management to collectively improve ecological and human health knowledge and management in a more coordinated fashion.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".