PRIO LLUV Radial Metrics Dataset Computed Using SeaSondeR R Package
Bibliographic record
Abstract
Overview This repository collects the Radial Metrics in CODAR's LLUV format obtained from the SeaSondeR package of the PRIO HF-Radar station maintained by INTECMAR. For more details on the processing of these data, please check: Herrera Cortijo, J. L., Fernández-Baladrón, A., Rosón, G., Gil Coto, M., Dubert, J., Montero, P., & Varela Benvenuto, R. (2025). Project HF-EOLUS. Task 2. Obtaining Radial Metrics from INTECMAR's VILA and PRIO Stations' Spectra. (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.16679609 Acknowledgements This work has been funded by the HF-EOLUS project (TED2021-129551B-I00), financed by MICIU/AEI /10.13039/501100011033 and by the European Union NextGenerationEU/PRTR - BDNS 598843 - Component 17 - Investment I3. Members of the Marine Research Centre (CIM) of the University of Vigo have participated in the development of this repository. Spectra from INTECMAR's PRIO HF-Radar station, between 2011-08-04 and 2023-11-23 have been transferred free of charge by the Observatorio Costeiro da Xunta de Galicia ( ) for their use. This Observatory is not responsible for the use of these data, nor is it linked to the conclusions drawn with them. The Costeiro da Xunta de Galicia Observatory is part of the RAIA Observatory ( ). We want to thank Dr. Pedro Montero from the INTECMAR for his help in providing the spectra.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.068 |
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".