HF RADAR - French Riviera (Mediterranean Institute of Oceanography) - hourly surface currents processed in real-time
Bibliographic record
Abstract
Hourly High Frequency Radar (HFR) surface current data (ocean surface velocity) from 2 different stations located on the French Mediterranean coast (Toulon), processed in real time. The total velocity is then reconstructed from the filled radial velocity files, and projected onto a cartesian grid of 1km x 1km. The HFR data comes from two systems, one monostatic radar PEY (located at Fort Peyras, La Seyne sur mer), and one bistatic POB (receiver located at Cap Bénat - Bormes les Mimosas, and transmitter on Porquerolles Island). The HFR data is initially hourly sampled. The radial velocity are processed by an algorithm of Directional of Arrival Finding with a self-calibration method developed by our laboratory. HF radar sites : - Peyras : 43°03'47.4"N, 5°51'40.3"E - Porquerolles (transmitter only): 42°58'59.0"N, 6°12'15.3"E - Bénat (receiver only): 43°05'31.5"N, 6°21'26.5"E EUROPEAN DIRECTORY OF MARINE ENVIRONMENTAL RESEARCH PROJECTS (EDMERP) : - SICOMAR PLUS(12402), IMPACT(12271), MOOSE(11574), and JERICO NEXT(12227) EQUIPEMENTS: - High Frequency Surface Wave radar WERA from HELZEL MESSTECHNIK PARAMETERS: - sea surface current Citation: - Dylan Dumas, Charles-Antoine Guerin, Self-calibration and antenna grouping for bistatic oceanographic High-Frequency Radars,2020, https://arxiv.org/abs/2005.10528
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".