Bibliographic record
Abstract
Github link: https://github.com/purnelldj/gnssir_rt # gnssir_rt This software is to go with "Real-time water levels using GNSS-IR: a potential tool for flood monitoring" by Purnell et al. (Geophysical Research Letters) All code written by David Purnell except for gnssr/make_gpt.py (written by Kristine Larson) ## Dependencies numpy, astropy, matplotlib, scipy ## How to use the code SNR data can be coverted to 'arcs' and then to a water level spline. Easiest to use from the command line as follows ``` python main.py [station] [funcname] ``` where `[station]` corresponds to a file: `site_inputs/[station].py` and funcname is one of 'snr2arcs', 'arcsplot' or 'arcs2splines' ## SNR data format SNR data has been provided to go with the paper, it can be found at: https://doi.org/10.5281/zenodo.10114719 The SNR data format is the same as https://gnssrefl.readthedocs.io/en/latest/pages/file_structure.html#the-snr-data-format but with differences on fourth and fifth columns. Instead of seconds of day in the fourth column it is GPS time: https://docs.astropy.org/en/stable/api/astropy.time.TimeGPS.html . The fifth column is L1 SNR (there are only five columns)
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.459 | 0.647 |
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".