Estimation of subsurface ocean density structure using remote sensing and data assimilation
Bibliographic record
Abstract
Recently, researchers from Dalhousie University, The Royal Military College, and the Defence Research Establishment Atlantic started work on estimating water density, and eventually underwater acoustical properties, from in situ and remotely sensed data. This requires the development of assimilation schemes to project vertically into the ocean interior the surface information gathered by remote sensing. Initially, we are concentrating on inferring the seasonal distributions of temperature and salinity on the continental shelf. We are experimenting with new ways of assimilating such data into fully nonlinear, baroclinic models using the incremental approach. In a nutshell, the approach uses two parallel ocean models: a complex model to be fit to the data, and a simple model with known adjoint that is used to correct the control variables of the complex model. The result is a set of seasonal fields that is consistent with the observed data, and dynamical constraints imposed by the model, within prescribed errors bars. Our long-term objective is to develop practical schemes for assimilating time sequences of remotely sensed (e.g., SST, ocean color, altimetry, HF radar) data into any ocean model. Progress will be reviewed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".