Analyzing pan-Arctic 1982–2006 trends in temperature and bioclimatological indicators (productivity, phenology and vegetation indices) using remote sensing, model and field data
Bibliographic record
Abstract
Warming induced changes in Arctic vegetation have to date been studied through \nobservational and experimental field studies, leaving significant uncertainty about \nthe representativeness of selected field sites as well as how these field scale findings \nscale up to the entire pan-Arctic. The purposes of this thesis were therefore to \n1) analyze remotely-sensed/modeled temperature, Normalized Difference Vegeta- \ntion Indices (NDVI) and plant Net Primary Productivity (NPP) to assess coarse- \nscale changes (1982–2006) in vegetation; and 2) compare field, remote sensing and \nmodel outputs to estimate limitations, challenges and disagreements between data \nformats. The following data sources were used: \n • Advanced Very High Resolution Radiometer Polar Pathfinder Extended (APP- \n x, temperature & albedo) \n • Moderate Resolution Imaging Spectroradiometer (MODIS, Normalized Dif- \n ference Vegetation Index (NDVI) & Enhanced Vegetation Index (EVI) ) \n • Landsat Enhanced Thematic Mapper (Landsat ETM, NDVI) \n • Global Inventory Modeling and Mapping Studies (GIMMS, NDVI) \n • Global Productivity Efficiency Model (GloPEM, Net Primary Productivity \n (NPP)) \nOver the pan-Arctic (1982-2007), increases in temperature, total annual NPP and \nmaximum annual NDVI were observed. Increases in NDVI and NPP were found to \nbe closely related to increases in temperature according to non-parametric Sen’ \nslope and Mann Kendall tau tests. Variations in phenology were largely non- \nsignificant but related to increases in growing season temperature. \n Snow melt onset and spring onset correspond closely. MODIS, Landsat and \nGIMMS NDVI data sets agree well, and MODIS EVI and NDVI are very similar \nfor spring and summer at Fosheim Peninsula. GloPEM NPP and field estimates \nof NPP are poorly correlated, whereas GIMMS NDVI and GloPEM NPP are well \ncorrelated, indicating a need for better calibration of model NPP to field data. \n In summary, increases in pan-Arctic biological productivity indicators were ob- \nserved, and were found to be closely related to recent circumpolar warming. How- \never, these changes appear to be focused in regions from which recent field studies \nhave found significant ecological changes (Alaska), and coarse resolution remote \nsensing estimates of ecological changes have been less marked in other regions. Dis- \ncrepancies between results from model, field data and remote sensing, as well as \ncentral questions remaining about the impact of increases in productivity on soil- \nvegetation-atmosphere feedbacks, indicate a clear need for continued research into \nwarming induced changes in pan-Arctic vegetation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".