Sediment microbial enzyme activity as an indicator of nutrient limitation in Great Lakes coastal wetlands
Bibliographic record
Abstract
Summary 1. We compared the extracellular enzyme activity (EEA) of sediment microbial assemblages with sediment and water chemistry, gradients in agricultural nutrient loading (derived from principal component analyses), atmospheric deposition and hydrological turnover time in coastal wetlands of the Laurentian Great Lakes. 2. There were distinct increases in nutrient concentrations in the water and in atmospheric N deposition along the gradient from Lake Superior to Lake Ontario, but few differences between lakes in sediment carbon (C), nitrogen (N) or phosphorus (P). Wetland water and sediment chemistry were correlated with the agricultural stress gradient, hydrological turnover time and atmospheric deposition. 3. The N : P ratio of wetland waters and sediments indicated that these coastal wetlands were N‐limited. Nutrient stoichiometry was correlated with the agricultural stress gradient, hydrological turnover time and atmospheric deposition. 4. Extracellular enzyme activity was correlated with wetland sediment and water chemistry and stoichiometry, atmospheric N deposition, the agricultural stress gradient and the hydrological turnover time. The ratios of glycosidases to peptidases and phosphatases yielded estimates of nutrient limitation that agreed with those based solely on nutrient chemistry. 5. This study, the first to link microbial enzyme activities to regional‐scale anthropogenic stressors, suggests that quantities and ratios of microbial enzymes are directly related to the concentrations and ratios of limiting nutrients, and may be sensitive indicators of nutrient dynamics in wetland ecosystems, but further work is needed to elucidate these relationships.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".