Oceanographic data assimilation and regression analysis
Bibliographic record
Abstract
A simple method is described for assimilating a set of irregularly spaced observations into a dynamically-based model of the coastal ocean. The method can be used with complex models of high dimension and is relatively efficient and effective. It is based on the use of a simpler model to reduce, in an iterative fashion, the mean square difference between the observations and the predictions of the complex model. To illustrate the method we use it to predict tidal sea-levels and currents in the Gulf of St. Lawrence, a semi-enclosed sea off Canada's east coast, from sea-levels measured by 19 coastal tide gauges. The method is shown to predict sea-levels to within several cm, and currents to within several cm s−1. To explain the method, we relate it to the familiar concept of nonlinear regression and the Gauss–Newton algorithm for the minimization of a multivariate function. Copyright © 2000 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".