Vegetation greening in the canadian arctic related to decadal warming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it