River Slope Analysis Using SWOT Node Data
Bibliographic record
Abstract
This dataset supports Table 1 of the associated publication and provides slope statistics derived from SWOT node-based water surface elevation (WSE) profiles. The analysis focuses on slope changes across the Chilcotin river between July and September 2024. Data Contents Excel (.xlsx) filesSWOT node data used to compute river slope profiles upstream and downstream of the landslide site for three observation dates: WSE_nodes_2024-07-12.xlsx WSE_nodes_2024-08-13.xlsx WSE_nodes_2024-09-08.xlsx Data sources and products SWOT WSE Node Data – Product: SWOT L2_HR_RiverSP_Node, accessed via NASA Earthdata Search Variables Each file includes the following columns: WSE (Water Surface Elevation) [m] 'p_dist_out' (distance to outlet) [m] Water surface elevation uncertainty ('wse_u') provided in the node product [m] X_UTM (UTM coordinate) [m] Time range SWOT acquisition dates: 2024-07-12, 2024-08-13, 2024-09-08 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: Slope_statistics.ipynb ↳ 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.033 |
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".