Improved estimation of oceanographic climatology using empirical orthogonal functions and clustering
Bibliographic record
Abstract
Vertical profiles of temperature, salinity, and sound speed velocity are used in numerous applications where accurate vertical profiles are crucial. Conventional climatological representations of vertical oceanographic profiles are based on mean or median profiles of historic data in a rectangular area containing the position in question. In areas containing oceanographic fronts mean profiles may not be representative for the profiles in the area and may even be unphysical. We propose a different approach to generate more realistic climatological estimates of the vertical profiles at a given time and position. The depth-dependent behaviours of all historic temperature and salinity profiles are classified by combining Empirical Orthogonal Function (EOF) analysis with K-means clustering. All profiles with similar EOF-coefficients are sorted into a single cluster and averaged to find a representative profile for that cluster. The geographical extent and temporal validity of the cluster are given by the positions and measurement times of the contained profiles. The method is here illustrated using ARGO temperature profiles from the North Atlantic from 2001 to 2012. The proposed method automatically allocates a high density of clusters in areas with large oceanographic variability, such as areas with oceanographic fronts. On the eastern coast of North America cold water from the Labrador Sea runs southwards between the coastline and the warmer Gulf Stream running northeast, resulting in strong fronts. The depth-dependent behaviour of an average profile from all profiles contained in a rectangular, geographic window may differ strongly from the present oceanographic profiles. The profiles representing the nearby clusters, on the other hand, better represent the general depth-dependent behaviour of the profiles in this region.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".