Seasonal effects of the zebra mussel (<i>Dreissena polymorpha</i>) on sediment denitrification rates in Pool 8 of the Upper Mississippi River
Bibliographic record
Abstract
Zebra mussels (Dreissena polymorpha) have altered the structure of invaded ecosystems and exhibit characteristics that suggest they may influence ecosystem processes such as nitrogen (N) cycling. We measured denitrification rates seasonally on sediments underlying zebra mussel beds collected from the impounded zone of Navigation Pool 8 of the Upper Mississippi River. Denitrification assays were amended with nutrients to characterize variation in nutrient limitation of denitrification in the presence or absence of zebra mussels. Denitrification rates at zebra mussel sites were high relative to sites without zebra mussels in February 2004 (repeated measures analysis of variance (RM ANOVA), p = 0.005), potentially because of high NO3-N variability from nitrification of high NH4+zebra mussel waste. Denitrification rates were highest in June 2003 (RM ANOVA, p < 0.001), corresponding with the highest NO3-N concentrations during the study (linear regression, R2 = 0.72, p < 0.001). Denitrification was always N-limited, but sites without zebra mussels showed the strongest response to N amendments relative to sites with zebra mussels (two-way ANOVA, p ≤ 0.01). Examining how zebra mussels influence denitrification rates will aid in developing a more complete understanding of the impact of zebra mussels and more effective management strategies of eutrophic waters.
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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 | 0.000 | 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".