Parameterization of biogeochemical sediment–water fluxes using in situ measurements and a diagenetic model
Bibliographic record
Abstract
Diagenetic processes are important drivers of water column biogeochemistry in coastal areas. For example, sediment oxygen consumption can be a significant contributor to oxygen depletion in hypoxic systems, and sediment–water nutrient fluxes support primary productivity in the overlying water column. Moreover, nonlinearities develop between bottom water conditions and sediment–water fluxes due to loss of oxygen-dependent processes in the sediment as oxygen becomes depleted in bottom waters. Yet, sediment–water fluxes of chemical species are often parameterized crudely in coupled physical–biogeochemical models, using simple linear parameterizations that are only poorly constrained by observations. Diagenetic models that represent sediment biogeochemistry are available, but rarely are coupled to water column biogeochemical models because they are computationally expensive. Here, we apply a method that efficiently parameterizes sediment–water fluxes of oxygen, nitrate and ammonium by combining in situ measurements, a diagenetic model and a parameter optimization method. As a proof of concept, we apply this method to the Louisiana Shelf where high primary production, stimulated by excessive nutrient loads from the Mississippi–Atchafalaya River system, promotes the development of hypoxic bottom waters in summer. The parameterized sediment–water fluxes represent nonlinear feedbacks between water column and sediment processes at low bottom water oxygen concentrations, which may persist for long periods (weeks to months) in hypoxic systems such as the Louisiana Shelf. This method can be applied to other systems and is particularly relevant for shallow coastal and estuarine waters where the interaction between sediment and water column is strong and hypoxia is prone to occur due to land-based nutrient loads.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".