Relationships among nutrients, phytoplankton, macrophytes, and fish in prairie wetlands
Bibliographic record
Abstract
Phytoplankton abundance and nutrient concentrations in shallow-water ecosystems are influenced by submerged macrophytes, zooplankton, and fish, but few studies have simultaneously assessed the influence of all three variables. We sampled 18 semipermanent prairie wetlands for 5 years to assess influences of minnows, submersed macrophytes, cladocerans, and drainage history on phytoplankton abundance and concentrations of nitrogen (N) and phosphorus (P). Our macrophyte data reflect the abundance of three distinct species assemblages (Chara, Potamogeton, and Myriophyllum assemblages) typical of these wetlands. Partial redundancy analysis showed only the Chara and Potamogeton assemblages and fish to be significantly related to algal abundance, N, and P. Macrophytes and fish together explained 40% of the total variance, but the Chara assemblage explained threefold, and the Potamogeton assemblage twofold, more variance than did presence/absence of fish. However, relationships with N and P differed for the two groups of macrophytes: P showed a strong negative relationship with both plant assemblages, and N showed a weak negative relationship with Chara but no relationship with Potamogetons. Our results indicate that phytoplankton and nutrient concentrations in prairie wetlands are strongly influenced by submersed macrophytes, although influences may depend on plant community composition.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".