A Basin-Wide Survey of Coastal Wetlands of the Laurentian Great Lakes: Development and Comparison of Water Quality Indices
Bibliographic record
Abstract
Coastal wetlands of the Laurentian Great Lakes are vital habitats for biota of ecological and economic importance. These habitats are susceptible to water quality impairments driven by runoff from the landscape due to their location along the shoreline. Monitoring of the overall status of biotic and abiotic conditions of coastal wetlands within the Great Lakes has been ongoing for over a decade. Here, we utilize measurements of aquatic physicochemical and land cover variables from 877 vegetation zones in 511 coastal wetland sites spanning the US and Canadian shorelines of the entire Great Lakes basin. Our objective is to develop water quality indices based on physicochemical measures (Chem-Rank), land use/land cover (LULC-Rank), and their combined effects (Sum-Rank and Simplified Sum-Rank), for both vegetation zones and wetland sites. We found that water quality differed among wetland vegetation types and among Great Lakes regions, corroborating previous findings that human land use alters coastal wetland water quality. Future monitoring can use these straightforward, easy-to-calculate indices to assess the abiotic condition of aquatic habitats. Our data support the need for management efforts focused on reducing nutrient and pollution loads that stem from human activities, particularly in the developed southern portions of the Great Lakes basin.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".