FOREST PHENOLOGICAL TRENDS IN THE MIDDLE AND HIGH LATITUDE OF THE NORTHERN HEMISPHERE
Bibliographic record
Abstract
Vegetation phenology is the study of periodically recurring patterns of growth and development of plants, which affect terrestrial ecosystem carbon, energy budget balance, fire disturbance, and climate– biosphere interactions. The increases in surface temperature had already altered the extent of vegetation phenology. Vegetation phenology can make some responses to climate factors, and the current climate change has attracted more research for the trend of vegetation phenology and its causes. The purpose of this paper is to investigate the spatial and temporal trend of forest phenology at mid and high latitude in the Northern Hemisphere (50°N-90°N, 180°W-180°E) over the period 2001–2017 using Collection 6 MODIS Land Cover Dynamics (MCD12Q2) datasets. The results indicated that SOS has a significant advanced trend, EOS has a significant delayed trend and LOS showed a significant extended trend on the whole. The significant advancement of SOS and extension of LOS mainly occurred in central Russia, the north and southwest of North America. Meanwhile, EOS showed a delayed trend in the south of Russia, the north and southwest of Canada and Alaska.
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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 | 0.000 |
| 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 | 0.000 | 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".