J. Great Lakes Res. 33 (Special Issue 3):67–85 Internat. Assoc. Great Lakes Res., 2007 Water Quality in Great Lakes Coastal Wetlands: Basin-wide Patterns and Responses to an Anthropogenic Disturbance Gradient
Bibliographic record
Abstract
ABSTRACT. We present water quality data from 58 coastal wetlands, sampled as part of a larger effort investigating effects of nutrient enrichment and habitat disruption in the Laurentian Great Lakes. Our sampling design selected sites from across a gradient of agricultural intensity within combinations of biogeographic ecoprovince and wetland hydromorphic type and captured a large range in water quality. Levels of total nutrients (N and P), and various measures of particulate concentration, water clarity, and ionic strength were strongly associated with agricultural intensity in the watershed, and could be effec-tively aggregated into an overall principal component-based water quality descriptor. Lake Erie wetlands had the highest nutrient levels and lowest water clarity, while wetlands in Lakes Superior and Huron had the lowest nutrient levels and clearest water. Lake Ontario wetlands had clearer water than would be expected from their nutrient levels and position on the agricultural intensity gradient. Dissolved oxygen, silica, pH, and dissolved organic carbon (DOC) were independent of agricultural intensity but DOC was responsible for low water clarity in some Lake Superior wetlands. Simple classification by hydromorphic type (riverine or protected) did not explain water quality differences among wetlands exposed to similar agricultural intensity levels, so finer hydrologic classification may be desirable. Results are used as a basis for discussing research and information needs underlying development of water quality criteria and monitoring programs for coastal wetlands of the Great Lakes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".