Modeling of nitrogen and phosphorus profiles in sediment of Osaka Bay, Japan with parameter optimization using the polynomial chaos expansion
Bibliographic record
Abstract
Coastal sediments adjacent to urban centers often receive high loads of organic matter (OM) due to large nutrient inputs from land that stimulate algae blooms. Early diagenetic models describing the remineralization of this OM in sediments have been developed for 50 years. Although these models can be applied to a range of marine sediments, specifying their model parameter values is difficult. In this study, one of the early diagnetic models was applied to simulate sediments in Osaka Bay, Japan and the polynomial chaos expansion (PCE) technique was used in order to choose optimal model parameters in the model. Following a sensitivity analysis, we estimated values for six parameters including the ratio of fast-decaying OM to total OM, the ratio of non-degradable OM to total OM, and the carbon–nitrogen ratio. Optimal parameter values were determined by minimizing the misfits between simulated and observed release rates of ammonium and phosphate from the sediments, and vertical profiles of inorganic nitrogen, and phosphorus in the porewater. Simulations with the optimized parameters successfully reduce a dimensionless root mean square error by 68% and agree better with the observed profiles and release rates than without parameter estimation.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".