GNSS-IR data for "Real-time water levels using GNSS-IR: a potential tool for flood monitoring"
Bibliographic record
Abstract
Organised SNR data used for GNSS-IR analysis in the article "Real-time water levels using GNSS-IR: a potential tool for flood monitoring" by David Purnell, Natalya Gomez, William Minarik and Gregory Langston. The directories 'rv3s' and 'sjdlr' contain SNR data corresponding to sites Trois-Rivières and Saint-Joseph-de-la-Rive, respectively. Software for processing the data can be found at: https://github.com/purnelldj/gnssir_rt SNR data is given as text files in the format specified here except for columns 4+: https://gnssrefl.readthedocs.io/en/latest/pages/file_structure.html#the-snr-data-format columns: 1. sat PRN with an offset such that GLONASS satellites are between 100-200 and Galileo are between 200-300 2. satellite elevation (degrees) 3. azi is satellite azimuth (degrees) 4. GPS time (seconds since 1980) 5. L1 SNR
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 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 | 0.002 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.024 |
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; both teacher heads agree on what is shown here.
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".