Datasets for sediment source partitioning and budgeting: Fitzsimmons Creek Watershed, BC, Canada
Bibliographic record
Abstract
The data repository includes the necessary shapefiles, raster datasets, and data tables to understand the detailed sediment budget (2014-2018), and historical sediment yield for the Fitzsimmons Creek Watershed, BC, Canada.The sediment budget data includes feature classes delineating the areas of interest (AOI) by landform (restricted to where significant change occurred based on fieldwork and preliminary morphometric change analysis), and summary tables of historical gravel removal, sedimentation in the run-of-river intake headpond, and net change lidar analysis for the 2014-2018 period. The DEM’s were first co-referenced and clipped to the area of interest, then a limit of detection was applied for gross measurements, and a DEM-based uncertainty analysis was performed. Additionally, the bed material fraction of sediment sources was estimated based on local grain size distribution estimates.The sediment yield (SY) data includes georeferenced historical air photos and UAV orthophotos from 1947 to 2023 with the topsets manually delineated, bathymetry rasters representing the paleolake and 2023 surfaces, and a 2023 DSM from the 2023 UAV survey. Finally, summary tables are provided for SY volumes, by period, for the delta and lake basin proximal to the delta.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.030 |
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".