Global Spatial–Temporal Variability in Terrestrial Productivity and Phenology Regimes between 2000 and 2012
Bibliographic record
Abstract
The productivity and phenology of vegetation are spatially and temporally variable ecosystem functions. Monitoring spatial–temporal patterns in these functions can improve our understanding of global change and natural ecosystem variability and inform management actions. Researchers typically focus on temporal changes within or among static regions and omit dynamics of spatial configuration. Our goal was to assess global spatial–temporal variability in productivity and phenology regimes between 2000 and 2012 using a temporally dynamic functional type classification. Fourteen functional types were defined for each year by clustering the annual sum and annual variability (seasonality) of the fraction of photosynthetically active radiation (fPAR)—a biophysical proxy for vegetation greenness or productivity—from the Moderate Resolution Imaging Spectrometer (MODIS). The fourteen functional types ranged from tundra (low cumulative fPAR and highly seasonal) to tropical forests (high cumulative fPAR and low seasonality). Variability in the mean of the fPAR metrics and in two spatial pattern metrics was assessed for each functional type. Many pixels changed from one cluster to another then back again, suggesting considerable short-term variability. Temporal variability in the mean of the fPAR metrics was relatively low, with changes instead primarily manifested in spatial pattern. Spatial pattern was most variable within tundra, grasslands, shrublands, and savannas. A dynamic classification demonstrated the variability in spatial patterns of primary productivity and can be used for future monitoring.
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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 | 0.002 | 0.002 |
| 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".