Comprehensive network hydraulic scaling datasets and associated resources (discharge, channel length surveys, watershed metadata, blueline network shapefiles and reference images)
Bibliographic record
Abstract
Wetted channel networks expand and contract throughout the year. Direct observation of this process can be made by multiple intensive surveys of a catchment throughout the year. Godsey et al. (2014) suggest that the extent of the wetted channel network scales with discharge at the outlet by a power law (L = αQ^β). Using this relationship, we developed a framework to assess variability in the extent of wetted channels as a function of β and the variability in streamflow Q (Lapides et al., In Review, https://eartharxiv.org/mc6np/). This resource constitutes the empirical basis for that study, a comprehensive dataset compiled from literature including: 1 - Channel length survey data (csv files) 2 - Discharge time series data (csv files) 3 - Watershed metadata (csv file) 4 - Blueline network files (pdf, png, and shp files) This collection is comprehensive in that it includes all watersheds where at least three channel length surveys have been conducted and where a corresponding discharge time series dataset is available. The requirement of a minimum of three channel length surveys stems from the data requirements to find α and β for the power law relationship between discharge and stream network length for headwater catchments (Godsey et al., 2014). At present, data for 14 watersheds worldwide are included in the collection along with reference maps, watershed metadata, shapefiles and a composite of USGS blueline stream network imagery with terrain for watersheds of interest in the United States. Notably, this collection brings data from a variety of earth science agencies worldwide into a common, clearly labelled format. Methods used to process the datasets or create other assets in this collection are included in the abstracts or additional metadata for each of the four resources listed above. Python code used to process data, compute variables, and create graphics is available at: https://zenodo.org/record/4057320
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.068 |
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".