A statistical overview and perspectives on data assimilation for marine biogeochemical models
Bibliographic record
Abstract
Marine biogeochemistry refers to the processes associated with the planktonic ecosystem of the ocean. These are central to nutrient, carbon, and energy cycling, as well as providing the basis of the marine food chain. The field is being revolutionized by new data types and observing platforms, as well as by improvements in ocean modelling brought about by increasing computer power. To further our understanding of these systems, statistical estimation and inference are needed to combine the information in these data with dynamic models to provide improved estimates for the ocean's biogeochemical (BGC) state and its parameters. Such methodologies are termed data assimilation (DA). This paper seeks to provide an overview of DA for the emerging area of marine BGC modelling. A statistical framework is offered, and DA methods that are applicable to the spatio‐temporal dynamic models and data that define the BGC problem are reviewed. In addition to this primer on current BGC DA approaches, we offer our perspectives on the challenges and future work necessary to advance this field. This work emerged from a symposium on marine BGC DA that took place in Hobart, Australia, on 28–30 May 2013. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".