The Laurentian Great Lakes in transition: A chronicle of research at the base of the foodweb
Bibliographic record
Abstract
A unique science and management strategy has been developed for the Laurentian Great Lakes due to their enormous size, geographic-ecological diversity, political and economic importance. This article is a documentary of more than 40 years of research conducted at the base of the foodweb by Fisheries and Oceans Canada, which has contributed significantly to the management of the Great Lakes. In the 1960s, the governments of Canada and the United States responded to the threat of cultural eutrophication which eventually resulted in the signing of the binational Great Lakes Water Quality Agreement. Dr. R. A. Vollenweider and Dr. J. R. Vallentyne were instrumental in developing a phosphorus abatement program, as well as the adoption of the “ecosystem approach” resulting in an holistic and integrated protocol for managing multiple environmental stressors. By showcasing some selected examples (Lake Ontario, Bay of Quinte, current research activities), an attempt is made to chronicle the evolution of phytoplankton, primary productivity and microbial foodweb research in the Great Lakes. Some of the research programs, techniques, models, policies and international cooperation are highlighted, in addition to the strong European influences on Great Lakes research. The lessons learned from the long-term Great Lakes research experience could be extrapolated and applied to enhance understanding of the ecology and management of other large lake ecosystems throughout the world.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".