Characterizing Vegetation Response to Climatic Variations in Hovsgol, Mongolia Using Remotely Sensed Time Series Data
Why this work is in the frame
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Bibliographic record
Abstract
One of the challenges faced by forest managers is the inability to quickly interpret forest ecosystem attributes and vegetation responses to climate change. This research aims to address this challenge by characterizing the phenological metrics and evaluating the temporal and spatial dynamics of vegetation over 12 years (2000-2011) under climate change effects in Hovsgol, Mongolia. Time series Normalized Difference Vegetation Index (NDVI) was used as an indicator to monitor vegetation response in the area. The effects of climatic variations on vegetation growth were considered through the relationship between climatic variables and NDVI. Results indicate that the growing season commonly starts in late April and ends in late October with full growth by July, and as a consequence of climate change in the area, the growing season in recent years seems to be beginning earlier. Plant stress caused by higher temperature was the most significant contributor to earlier vegetation green up since NDVI, length, and starting point of the growing season strongly depend on air temperature. Analysis of spatio-temporal heterogeneity indicates some areas with highly dynamic NDVI, particularly in the western part of the Hovsgol Lake, the high mountainous areas, and the Darhad valley. Our results suggest that temperature variations mainly determine the pattern of vegetation responses in the Hovsgol area.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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