A Statistical Method for Correcting Salinity Observations from Autonomous Profiling Floats: An ARGO Perspective
Bibliographic record
Abstract
Abstract The Profiling Autonomous Lagrangian Circulation Explorer (PALACE) float is used to implement the Array for Real-Time Geostrophic Oceanography (ARGO). This study presents a statistical approach to correct salinity measurement errors of an ARGO-type fleet of PALACE floats. The focus is on slowly evolving drifts (typically with time scales longer than a few weeks). Considered for this case study is an ensemble of about 80 floats in the Irminger and Labrador Seas, during the 1996–97 period. Two different algorithms were implemented and validated based on float-to-float data comparison at depth, where the water masses are relatively stable over the time scales of interest. The first algorithm is based upon objective analysis of the float data, while the second consists of a least squares adjustment of the data of the various floats. The authors’ method exhibits good skills to retrieve the proper hydrological structure of the case study area. It significantly improves the consistency of the PALACE dataset with in situ data as well as with satellite altimetric data. As such, the method is readily usable on a near-real-time basis, as required by the ARGO project.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".