An Argo mixed layer climatology and database
Bibliographic record
Abstract
Abstract A global climatology and database of mixed layer properties are computed from nearly 1,250,000 Argo profiles. The climatology is calculated with both a hybrid algorithm for detecting the mixed layer depth (MLD) and a standard threshold method. The climatology provides accurate information about the depth, properties, extent, and seasonal patterns of global mixed layers. The individual profile results in the database can be used to construct time series of mixed layer properties in specific regions of interest. The climatology and database are available online at http://mixedlayer.ucsd.edu . The MLDs calculated by the hybrid algorithm are shallower and generally more accurate than those of the threshold method, particularly in regions of deep winter mixed layers; the new climatology differs the most from existing mixed layer climatologies in these regions. Examples are presented from the Labrador and Irminger Seas, the Southern Ocean, and the North Atlantic Ocean near the Gulf Stream. In these regions the threshold method tends to overestimate winter MLDs by approximately 10% compared to the algorithm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.009 | 0.008 |
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".