HF RADAR - French Riviera (Mediterranean Institute of Oceanography) - daily surface currents filled with DINEOF
Bibliographic record
Abstract
Daily High Frequency Radar (HFR) surface current data (radial velocity files and total velocity file) from 2 different stations located on the French Mediterranean coast (Toulon), spanning from January 2012 to December 2019. The radial datasets have been processed to remove outliers. Then, the gaps in the data have been filled using the DINEOF algorithm. 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 (emitter located at Cap Bénat - Bormes les Mimosas, and transmitter on Porquerolles Island). The HFR data is initially hourly sampled. To remove the outliers of the data, for each timestep, a Probability Density Function (PDF) is computed on the spatial gradient of each radial map. Pixels with a spatial gradient with a probability under 3% are removed. Additionnally, for each pixel, a PDF is computed on the temporal gradient of its whole timeseries. Timesteps with a temporal gradient that have a probability under 1% are then removed. Then we proceed to a preliminary temporal and spatial hole filling of the missing data. For the timeseries of each pixel, timesteps that are surrounded by valid values within 3 hours (i.e. 3 timesteps) are filled by a weighted linear interpolation. For each timestep, pixels of the map surrounded by values within 1 grid point are filled in the same way. The radial data is then daily averaged. The DINEOF algorithm (http://modb.oce.ulg.ac.be/mediawiki/index.php/DINEOF) is run in a multivariate way (2 radial velocity files) using 50 EOF modes for the reconstruction. At some timesteps (shown by the flag variable of the file), the filling has not been possible, and the missing maps have been replaced by the temporal average radial map. The filled radial velocities are then locally interpolated onto a cartesian grid of 1km spatial resolution using a Weighted Least Square method. 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 JERICHO NEXT(12227) EQUIPEMENTS: - High Frequency Surface Wave radar WERA from HELZEL MESSTECHNIK PARAMETERS: - sea surface current
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".