Lakes of Alaska with subsetting by watershed and SASAP region
Bibliographic record
Abstract
Lakes of Alaska were analyzed by watershed and region of Alaska for the State of Alaska Salmon and People (SASAP) project. 188 watersheds and thirteen regions were assessed for number of intersecting lakes, area covered by lakes, and other lakes statistics as described in "Lake_Polygons.ipynb" in this package. Lakes data is based on the River Analysis Project (RAP) dataset as outlined in "A Riverscape Analysis Tool Developed to Assist Wild Salmon Conservation Across the North Pacific Rim" by Whited, D. C., J. S. Kimball, J. A. Lucotch, N. K. Maumenee, H. Wu, S. D. Chilcote, and J. A. Stanford (2012). Lakes polygons in the RAP dataset were provided directly by the authors (Waterbody_AK_RAP.zip) and are based on 1990s-era Landsat 4 and 5 satellite data, with methodology outlined in the publication above. More information on RAP can be found here: http://www.ntsg.umt.edu/rap/default.php. Regions are determined by the SASAP project and watersheds are based on HUC8 definitions from the International Joint Commission and sub-sub-drainages from the Canadian National Hydro Network.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.013 |
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".