DOI 10.1007/s00382-007-0339-5 Terrestrial vegetation dynamics and global climate controls
Bibliographic record
Abstract
Abstract Monthly data from the moderate resolution imaging spectroradiometer (MODIS) and its predecessor satellite sensors was used to reconstruct vegetation dynamics in response to climate patterns over the period 1983–2005. Results suggest that plant growth over extensive land areas of southern Africa and Central Asia were the most closely coupled of any major land area to El Niño–southern oscillation (ENSO) effects on regional climate. Others land areas strongly tied to recent ENSO climate effects were in northern Canada, Alaska, western US, northern Mexico, northern Argentina, and Australia. Localized variations in precipitation were the most common controllers of monthly values for the fraction absorbed of photosynthetically active radiation (FPAR) over these regions. In addition to the areas cited above, seasonal FPAR values from MODIS were closely coupled to rainfall patterns in grassland and cropland areas of the northern and central US. Historical associations between global vegetation FPAR and atmospheric carbon dioxide (CO2) anomalies suggest that the terrestrial biosphere can contribute major fluxes of CO2 during major drought events, such as those triggered by 1997–1998 El Niño event.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.904 | 0.909 |
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".