Nearly 50 years of water quality monitoring shows improvements and remaining challenges for a delisted Great Lakes Area of Concern
Bibliographic record
Abstract
Severn Sound, located in Lake Huron, is one of nine (out of 43) Areas of Concern (AOC) that have been delisted. In 1987, Severn Sound was designated an AOC due to eutrophication and habitat loss, and after mitigation measures, was delisted in 2003. Information used to delist Severn Sound was based in part on a long-term water quality program by the Ministry of the Environment and Severn Sound Environmental Association that has been uninterrupted since 1973. Such temporally extensive water quality programs are rare in the Great Lakes, especially those that capture decadal periods of environmental change driven by government regulations, species invasions, and climate change. To improve our understanding of long-term water quality in Severn Sound, we applied a suite of statistical methods to assess trends and patterns of change across four embayments from 1973 to 2020 for the following water quality variables: chlorophyll a , ammonia + ammonium, nitrate + nitrite, total organic nitrogen, total nitrogen, total phosphorus, Secchi disk visibility, and water temperature. We found that the embayments in Severn Sound have significant differences across stations in water quality parameters such as total phosphorus, total nitrogen, Secchi disk visibility, and water temperature. Overall decreases in total phosphorus and chlorophyll a and increases in Secchi disk visibility illustrate the influences of nutrient reduction strategies and potential influences from invasive dreissenid mussels. Although an important driver of water quality changes, surface water temperature change was variable during the study, likely due to site-specific differences and inherent data variability over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".