Elevation data to accompany "A curvature-based method for measuring valley width applied to glacial and fluvial landscapes"
Bibliographic record
Abstract
This repository contains elevation data, derivatives, and manual measurements used in the manuscript "A curvature-based method for measuring valley width applied to glacial and fluvial landscapes" submitted to the Journal of Geophysical Research. This data is derived from the 1/3 arc-second (~10 m) resolution seamless digital elevation models from the 3D Elevation Program (3DEP) in the coterminous United States [0]. The Canadian Rockies study site uses 90 m resolution data derived from the Shuttle Radar Topography Mission (SRTM) dataset [1]. Each study site corresponds to a single directory (e.g. 'valley_width/olympics/'). Derivatives are stored in GeoTIFF format (with no file extensions) with the following naming conventions (shown for the Olympic Mountains study area). Manual width measurements are stored as Pickle files (e.g. 'olympics_width_fluvial.p') in the measurements/ subdirectory in each case. - olympics_area (Catchment area) - olympics_elevation (Elevation) - olympics_filled (Hydrologically corrected elevation) - olympics_flow_direction (Flow direction) - olympics_mask_fluvial (Binary mask of fluvial catchments) - olympics_mask_glacial (Binary mask of glacial catchments) - olympics_unnormalized_width (Valley width estimated without using scale normalization) - olympics_width (Valley width estimated using scale normalization) Python scripts to reproduce the major figures and analysis and their EPS output are also included. References [0] https://www.usgs.gov/core-science-systems/ngp/3dep/about-3dep-products-services [1] http://srtm.csi.cgiar.org/srtmdata/
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.237 | 0.152 |
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".