Effects of sediment nutrients and depth on small-scale spatial heterogeneity of submersed macrophyte communities in Lake Pleasant, Pennsylvania
Bibliographic record
Abstract
Sediment concentrations of total and available nitrogen (N), phosphorus (P), and potassium (K) and organic matter from the littoral zone of Lake Pleasant, Pennsylvania, were highly variable. Only organic matter and total N were correlated with depth, however. This result suggests the existence of more complex environmental gradients than the prevailing paradigm of monotonic changes in sediment characteristics with increasing depth. The spatial heterogeneity of submersed aquatic plant communities was significantly correlated with depth, and available N and P. Canonical correspondence analysis demonstrated that these three factors explained 38% of the variance in community structure. Other sediment characteristics (available K, organic matter, and total N, P and K) were not significant by themselves, but all variables combined explained 63% of community-structure variance. Cluster analysis identified species or groups of species typical of endpoints on the depth versus nutrient axes. Myriophyllum exalbescens was typical of deep sites with relatively nutrient-rich sediments, whereas deep nutrient-poor sites were dominated by Vallisneria americana and Megalodonta beckii. Shallow nutrient-rich sites were dominated by several species of Potamogeton and Elodea canadensis, and shallow nutrient-poor sites were dominated by Heteranthera dubia and Najas flexilis. These results demonstrate the importance of sediment characteristics in determining macrophytes' community structure within lakes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".