Parameter, State and Uncertainty Estimation for 3-dimensional Biological Ocean Models
Bibliographic record
Abstract
Realistic physical-biological ocean models pose challenges to statistical techniques due to their complexity, nonlinearity and high dimensionality. In this thesis, statistical data assimilation techniques for parameter and state estimation are adapted and applied to biological models. These methods rely on quantitative measures of agreement between models and observations. Eight such measures are compared and a suitable multiscale measure is selected for data assimilation. Build on this, two data assimilation approaches, a particle filter and a computationally efficient emulator approach are tested and contrasted. It is shown that both are suitable for state and parameter estimation. The emulator is also used to analyze sensitivity and uncertainty of a realistic biological model. Application of the statistical procedures yields insights into the model; e.g. time-dependent parameter estimates are obtained which are consistent with biological seasonal cycles and improves model predictions as evidenced by cross-validation experiments. Estimates of model sensitivity are high with respect to physical model inputs, e.g river runoff.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".