Vegetation greening in the canadian arctic related to decadal warming
Bibliographic record
Abstract
This study is presented within the context that climate warming and sea-ice decline has been occurring throughout much of the Arctic over the past several decades, and that terrestrial ecosystems at high latitudes are sensitive to the resultant alterations in surface temperatures. Results are from analyzing interannual satellite records of vegetation greenness across a bioclimate gradient of the Canadian Arctic over the period of 1982-2006. Here, we combine multi-scale sub-pixel analysis and remote sensing time-series analysis to investigate recent decadal changes in vegetation greenness along spatial gradients of summer temperature and vegetation. Linear autoregression temporal analysis of vegetation greenness was performed with relatively "pure" vegetation pixels of Advanced Very High Resolution Radiometer (AVHRR) data, spanning Low Arctic, High Arctic and polar desert ecosystems. Vegetation greenness generally increased over tundra ecosystems in the past two decades. Peak annual greenness increased 0.49-0.79%/yr over the High Arctic where prostrate dwarf shrubs, forbs, mosses and lichens dominate and 0.46-0.67%/yr over the Low Arctic where erect dwarf shrubs and graminoids dominate. However, magnitudes of vegetation greenness differ with length of time series and periods considered, indicating a nonlinear response of terrestrial ecosystems to climate change. The decadal increases of greenness reflect increasing vegetation production during the peak of the growing season, and were likely driven by the recent warming.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".