From Lakes to Plants: Spatial Assessment of Vegetation Dynamics Along 52 Disappearing Lakes in the Boreal Forests of Alaska from 1984-2009 in the Yukon Flats Wildlife Refuge
Bibliographic record
Abstract
A spatial assessment of vegetation change along the areas of shrinking lakes was conducted in 2011. The analysis utilized Landsat TM/ETM imagery bands 3 and 4 to calculate NDVI (normalized difference vegetation index) from 1984 to 2009 and applied cell and zonal statistics to measure the change as well as create an animation of the change. Also, three 30 meter bands and the zonal averages for the maximum values were assessed. The band in the 60-90 buffer bands has the greatest ratio amount of the maximum vegetation values when compared to the 0-30 meter and the 30-60 meter buffered bands. Additionally, there appears to be slight trends towards higher maximum vegetation values when it in dryer, cooler and later season.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".