Ensemble Kalman filter application for an ocean biogeochemical model in an idealized 3-dimensional channel
Bibliographic record
Abstract
The Matlab code included in this repository is used to perform the deterministic formulation of the Ensemble Kalman Filter (DEnKF) in the Regional Ocean Modelling System (ROMS). This code was developed by the MEMG group at Dalhousie University, Canada. It is a supplement to the paper "Ocean biogeochemical modelling" by K. Fennel, J.P. Mattern, S.C. Doney, L. Bopp, A.M. Moore, B. Wang, and L. Yu. in Nature Reviews Methods Primers (Sept 2022). The code in this repository is configured for a 3-dimensional ocean biogeochemical model (using ROMS) in an idealized channel that experiences wind-driven upwelling as in Yu et al. (2018, doi.org/10.1016/j.ocemod.2018.04.005). This code will initiate the ensemble runs with perturbed wind forcing and biological parameters, then assimilate observations to update the model state variables, and restart the ensemble runs from the updated initial state. In this application, we have two assimilation steps. In the first step, we assimilate the physical observations (i.e., sea surface height, sea surface temperature, and in-situ profiles of temperature) to update both physical and biological model state variables (i.e., temperature and NO₃). In the second step, we assimilate the biological observations (i.e., surface chlorophyll and in-situ profiles of NO₃) to update only biological model state variables (i.e., chlorophyll, phytoplankton, zooplankton, and NO₃).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".