Percent landcover per SASAP region and Hydrolic Unit (HUC8) boundary for Alaskan watersheds
Bibliographic record
Abstract
This dataset was created to assess the percentages of various landcovers in each SASAP region and each HUC8 watershed of Alaska (forest/shrub/barren/wetland). The 2010 National Land Cover Dataset ( "2005 North American Land Cover at 250 m spatial resolution. Produced by Natural Resources Canada/Canadian Center for Remote Sensing (NRCan/CCRS), United States Geological Survey (USGS); Insituto Nacional de Estadística y Geografía (INEGI), Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO) and Comisión Nacional Forestal (CONAFOR).”) was combined with the "sasap_regions.zip" shapefile (located here: https://knb.ecoinformatics.org/#view/urn:uuid:2c1c26fc-bfb9-4b6a-8d4a-0be8e61c4deb) to create the shapefile, "regions_slope_shp.zip" and with the "sasap_watersheds_gapfix.zip" shapefile (located here: https://knb.ecoinformatics.org/#view/urn:uuid:2b5ab57e-38ec-4bc9-8290-f080ec0befb4) to create the shapefile "watersheds_slope_shp.zip". CSV versions of the resulting shapefiles are also archived. The included python script, which was used to merge the data, outlines the process in more detail. Also included in this dataset are two figures. One shows the human footprint for Alaska and the western United States, which is derived from the original 2010 National Land Cover raster. The second is a figure showing forest cover by watershed in Southeast Alaska, created using code found in 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/F1MP51JD.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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; both teacher heads agree on what is shown here.
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".