Bibliographic record
Abstract
To assess temporal greenness trends at the landscape scale for Whitehorse, Yukon (417 km2), this study derived a Landsat normalized difference vegetation index (NDVI) time-series from 1984 to 2009. Using a greenest annual pixel approach, NDVI trend analysis revealed that 37% of studied area had significant greening (p<0.05) and that only 1% of the studied land area had significant browning. Yearly mean NDVI values declined in drought years and increased in years with greater precipitation. Greening pixels were most prevalent in white spruce (Picea glauca) and subalpine fir (Abies lasiocarpa) dominant forests, suggesting that increased amounts of precipitation and rising temperatures have benefited both species and associated shrub communities. Forests where trembling aspen (Populas tremuloides) are dominant displayed the least greening, which may be explained by the proliferation of aspen serpentine leaf miner (Phyllocnistis populiella), and drought related die-back on south-facing slopes that have become warmer across the study period.
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 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 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".