Bibliographic record
Abstract
This dataset contains data and scripts used to evaluate the agreement between SWOT Water Surface Elevation (WSE) node measurements and a Digital Elevation Model (DEM) from the Government of British Columbia. The comparison is performed through scatter plot visualization and statistical analysis (R², RMSE, MAE, bias) for multiple acquisition dates. Data contents: 'DEM_WSE validation' Comparison Table (.xlsx):A spreadsheet containing side-by-side elevation values from SWOT WSE nodes and corresponding DEM elevations for each acquisition date. LiDAR derived DEM Tile within Study Area (.tif)GeoTIFF tile representing LiDAR derived DEM data used for validation. Raw SWOT nodes (.nc) and nodes after quality flags filtering and clipping to the study area (.shp). Data sources and products SWOT WSE Node Data – Product: SWOT L2_HR_RiverSP_Node, accessed via NASA Earthdata Search DEM – Government of British Columbia, accessed via LiDAR Download Portal Variables WSE (Water Surface Elevation) [m] Elevation (DEM) [m] Time range SWOT acquisition dates: 2024-05-21, 2024-07-12, 2024-08-02, 2024-08-13, 2024-09-08 DEM acquisition date: 2024-05-20 Spatial coverage Study area: Chilcotin River, British Columbia, Canada Approximate bounding box: [51.87084,-122.82549; 51.87078,-122.72822; 51.83110,-122.72724; 51.83110,-122.82544] Script files included: Python Jupyter Notebook (scatter_plot_analysis.ipynb) that produces scatter plots and calculates statistical performance indicators (R², RMSE, MAE, bias) for each acquisition date, enabling direct comparison between SWOT WSE node measurements and DEM elevations.↳ Documented in: README.md
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.025 |
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".