Rivers of Alaska grouped by SASAP region, 2018
Bibliographic record
Abstract
This dataset contains a shapefile (rivers_sasap_final.zip) of the rivers of Alaska, grouped by the regional boundaries of the State of Alaska's Salmon and People (SASAP) project. This is a relatively lightweight file useful for visualization, even at high resolution. Note that because of the aggregation process, this dataset does not contain information on river names or other characteristics. The intermediate file shapefile sasap_rivs_connected_large.zip was generated originally to calculate river distance between points, and is topologically correct. The source data for this dataset comes from the United States Geological Survey (USGS) Hydrographic Geodatabase - Alaska, One Million-Scale Dataset (https://nationalmap.gov/small_scale/atlasftp.html) and the Riverscape Analysis Project river's database (to get Canadian data only). These two datasets were pre-processed using CanadaRAPclip.ipynb and QGIS, creating the larger intermediate shapefile (sasap_rivs_connected_large.zip). This shapefile was joined with a SASAP region shapefile (Jared Kibele. 2018. State of Alaska's Salmon and People Regional Boundaries. Knowledge Network for Biocomplexity. doi:10.5063/F1J964NZ), geometries were simplified and then aggregated by region and stream order using rivers_preprocessing.R More information on the Riverscape Analysis Project is available here: Diane C. Whited , John S. Kimball , John A. Lucotch , Niels K. Maumenee ,Huan Wu , Samantha D. Chilcote & Jack A. Stanford (2012) A Riverscape Analysis Tool Developed to Assist Wild Salmon Conservation Across the North Pacific Rim, Fisheries, 37:7,305-314, DOI: 10.1080/03632415.2012.696009.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.055 |
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".