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. Data is organised by sites: 'rv3x' is Trois-Rivières 'sjrx' is Saint-Joseph-de-la-Rive The 'x' at the end signifies that there is data from multiple co-located antennas at these sites (see details about naming format below**) SNR data is given as text files in the format specified here: https://gnssrefl.readthedocs.io/en/latest/pages/file_structure.html#the-snr-data-format Each line is coded in Python using: "{0:3.0f} {1:10.4f} {2:10.4f} {3:10.0f} {4:7.2f} {5:7.2f} {6:7.2f} {7:7.2f} {8:7.2f} \n".format(sat, elv, azi, sod, 0, 0, snr, 0, 0) where 'sat' is the satellite PRN identifier, with an offset such that GLONASS satellites are between 100-200 and Galileo are between 200-300 elv is satellite elevation (degrees) azi is satellite azimuth (degrees) sod is seconds of day snr is L1 SNR The key difference from the gnssrefl snr data format is the naming of the files. Each file is of the format: sssxYYMMDDHH.snr **where sssx is the station ID ('sjr' or 'rv3') + the letter a, b, c or d to indicate the four co-located antennas For example, 'rv3a20090917.snr' comprises of data from antenna 'a' at Trois-Rivières on 9th September 2020 at 17h (UTC) 'sjrd21111302.snr' comprises of data from antenna 'd' at Saint-Joseph-de-la-Rive on 13th November 2021 at 02h (UTC) Each file contains one hour of data (as opposed to 24 hours in the gnssrefl format).
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.147 | 0.128 |
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".