Episodic Nutrient Addition Affects Water Column Nutrient Processing Rates in River‐to‐Lake Transitional Zones
Bibliographic record
Abstract
Abstract Storm‐driven nutrient loading from tributaries can fuel eutrophication in nearshore and open water areas of lentic ecosystems. However, nutrient processing in river‐to‐lake transitional zones can substantially alter the amount and composition of nutrients transported to lakes from upstream surface waters. We measured the removal of nutrients and dissolved organic carbon (DOC) from the water column in the Fox rivermouth (Green Bay, Lake Michigan) to evaluate the response of rivermouth plankton to episodic nutrient enrichment. Light and dark water column incubations (8–12 hr) were conducted on four occasions from April through September to measure changes in dissolved nitrogen (N), phosphorus (P), and DOC concentrations in three locations along the Fox rivermouth. Two incubation experiments were conducted on consecutive days, (a) under ambient nutrient concentrations, and (b) under experimentally enriched N and P concentrations. Spatial and temporal variation was observed in nutrient uptake rates, but light incubations consistently had higher nutrient uptake rates than dark incubations. Nutrient enrichment increased total dissolved P and total dissolved N uptake and DOC release in light incubations, but only increased total dissolved P uptake in dark incubations. Moreover, nutrient uptake ratios (N:P) decreased from ambient to nutrient enriched conditions and indicated preferential P uptake by phytoplankton communities in light conditions. Our study substantiates that rivermouths can process nutrients bound for downstream ecosystems and demonstrates the potential of plankton communities to dynamically increase net uptake rates in response to episodic nutrient enrichment.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".