Modeling the Interplay between Phosphorus Dynamics and Sediment Diagenesis in a Eutrophic Lake
Bibliographic record
Abstract
In this study, we investigated phosphorus cycling in the Bay of Quinte, an embayment of Lake Ontario, Canada. Despite the large decline in external P loading to the Bay of Quinte during the last decades, it still experiences harmful cyanobacterial algal blooms, which may be connected to nutrient loading from sediments. Nevertheless, the dynamics of nutrient loading from sediments remains mostly unknown. Thus, we applied a non-steady-state diagenesis reaction–transport model to evaluate the impact of organic matter loading on oxygen demand and to conduct specific modeling scenarios to investigate the impact of organic matter loading on the seasonal dynamics of phosphorus release, and burial efficiency in three different basins of the bay: Belleville, Napanee, and Hay Bay. Our modeling framework integrates physical and biogeochemical processes at the sediment–water interface and incorporates dynamic boundary conditions, such as oxygen, soluble reactive phosphorus concentrations, and organic matter sedimentation at the sediment–water interface. Our scenarios suggested that phosphorus release and burial efficiency can profoundly respond to shifts in sedimentation conditions. At all three studied stations, phosphorus burial efficiency did not change significantly after the scenario year 2034, when we reduced the flux of organic matter by 20%. Meanwhile, phosphorus release at stations Belleville and Napanee was significantly reduced in 2034 compared with the present condition. The 20% reduction in the flux of organic matter at station Hay Bay may not be large enough to remarkably reduce phosphorus release. Keywords: Eutrophic lakes, phosphorus release; phosphorus burial efficiency, sediments; diagenetic modelling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".