River-to-Lake Transitional Areas Contribute Disproportionately to In-Lake Nutrient Loading
Bibliographic record
Abstract
River-to-lake transitional areas are biogeochemically active sections of the aquatic continuum that are often understudied compared to their adjoining environments. Internal nutrient loading from river-to-lake transitional areas may be a considerable source of nutrients to lakes and if overlooked disconnect upstream management initiatives from in-lake improvements. To contextualize internal nutrient loading by river-to-lake sediments, we conducted sediment core incubations and nutrient assays at 3 time points over a field season from a major contributing tributary of Lake Erie. Using statistical and spatial interpolation models, we upscaled internal nitrogen and phosphorus loading rates across the highly impaired mouth of the Maumee River, which drains into the western basin of Lake Erie. We found that internal nutrient dynamics in this river-to-lake transitional area were regulated by spatial differences in the physical composition and nutrient and organic matter contents of sediments. The Maumee river-to-lake transitional area was largely a source of phosphorus and ammonium nitrogen and a sink of nitrate nitrogen through high denitrification rates. Yet, we observed substantial temporal variation whereby internal nutrient loading was greatest in late summer coinciding with near-zero denitrification. Sediments at this time could contribute an additional ~17% more soluble reactive phosphorus and ~3% more total kjeldahl nitrogen in the bioavailable ammonium nitrogen fraction relative to the daily external nutrient load. High internal nutrient loading rates compared to more offshore areas in western Lake Erie suggest that this degraded river-to-lake transitional area has a disproportional biogeochemical significance and a high potential to contribute to nearshore water quality issues.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".