Code to Support Chinook Salmon Freshwater Habitat Potential Modeling and Mapping for the Yukon and Kuskokwim River Basins in Alaska
Bibliographic record
Abstract
Code to support the "Freshwater Habitat Potential for Chinook Salmon in the Yukon and Kuskokwim River Basins, Alaska" project dataset. These Python notebooks and scripts process synthetic stream networks and DEMs to create unique reach contributing areas (uRCAs) and uRCA valley bottoms (uRCA VBs). The Python notebooks are designed to be run within ESRI ArcPro software and utilize the 'arcpy' module. The data were processed by region and in sections based on National Hydrography Dataset (NHD) Level 8 hydrologic units (HUC8) to reduce the computing workload, and therefore the scripts make use of a naming convention based on HUC8 codes within each regional geodatabase. See metadata records for individual data elements for a description of input sources, software environments, data quality, processing steps, and attribute information.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.114 | 0.067 |
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".