Elevation per SASAP region and Hydrologic Unit (HUC8) boundary for Alaskan watersheds
Bibliographic record
Abstract
This dataset was created to assess regions and watersheds of Alaska for mean elevation, minimum elevation, maximum elevation, median elevation, standard deviation of elevation, range of elevation and coefficient of variation of elevation in each SASAP region and each HUC8 watershed of Alaska. Three DEM's were mosaicked to make an Alaska-wide tiff. These include separate files for Alaska, the Yukon, and British Columbia. They were combined with the "sasap_regions.zip" shapefile (Jared Kibele and Jeanette Clark. 2018. State of Alaska's Salmon and People Regional Boundaries. Knowledge Network for Biocomplexity. doi:10.5063/F1125QWP) to create the shapefile, "regions_elevation_shp.zip" and with the "sasap_watersheds_gapfix.zip" shapefile (Jared Kibele. 2018. Hydrologic Unit (HUC8) Boundaries for Alaskan Watersheds. Knowledge Network for Biocomplexity. doi:10.5063/F1Q52MV3.) to create the shapefile "watersheds_elevation_shp.zip". CSV versions of the resulting shapefiles are also archived. The included jupyter notebook which was used to merge the data, outlines the process in more detail. The included RMarkdown document is used to generate region and statewide figures for elevation, utilizing a set of functions written to map data for the SASAP project (Jeanette Clark, Rachel Carlson, and Jared Kibele. General mapping functions for data associated with the State of Alaska's Salmon and People (SASAP) project, 2019. Knowledge Network for Biocomplexity. doi:10.5063/F1Z31WXD).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 teacher head, 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".