Yukon River Basin ecosystem performance anomaly mapping
Bibliographic record
Abstract
The Yukon River Basin (YRB) is contained in Alaska, Yukon Territory and British Columbia. Under Annex 17 of the MOU between NRCan/CCRS and USGS/EROS annual maps of ecosystem performance anomalies were produced. Growing season average Normalized Difference Vegetation Index (NDVI) was produced from MODIS with 250 m resolution for years 2000-2005 and used as a proxy for ecosystem performance. A regression tree model for site potential was generated using permafrost (Ice Content, Extent and Landform), surface geology, elevation, slope/aspect, Compound Topographic Index (CTI), ecoregions, clusters, solar radiation, and Annual Moisture Index (AMI). A performance model included the site potential model and weather data including maximum temperature, minimum temperature, precipitation, and growing degree days for four periods each year. The predominant land cover type, boreal forest, was used, and excluded areas burnt in the previous 30 years. The model was trained using over 15,000 points from different years and different productivities. The residual between measured performance and the modelled performance was calculated. The top 10% was classified as over performing and the bottom 10% was classified as underperforming. An anomaly map was generated for each year of the study. Comparison of forest fire perimeters and detected anomalies show good correspondence. Further work is required to identify the cause of additional anomalies. Possible other causes include insect infestation, land cover change, or changes to drainage conditions from permafrost degradation. The annual anomaly maps were analyzed to find persistent or emerging over and under performance areas.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".