Trends in North American net primary productivity derived from satellite observations, 1982–1998
Bibliographic record
Abstract
Net primary productivity (NPP) in North America was computed for the years 1982–1998 using the Carnegie‐Ames‐Stanford approach (CASA) carbon cycle model. CASA was driven by a new, corrected satellite record of the normalized difference vegetation index at 8‐km spatial resolution. Regional trends in the 17‐year NPP record varied substantially across the continent. Croplands and grasslands of the Central Plains and eastern Canadian forests experienced summer increases in NPP. Peak NPP trends in Alaska and western Canada occurred in late spring or early summer, suggesting an earlier onset of the growing season in these regions. Forests and woodlands of the southeastern United States showed NPP increases in spring and fall, also suggesting an increase in the length of the growing season. An analysis of climate variables showed that summer precipitation increased in the Central Plains, indicating that climate changes probably play some role in increasing NPP in this region, though intensive management of agricultural ecosystems has also increased productivity. Similarly, increased summer precipitation possibly increased NPP in eastern Canada, but another possible explanation is forest recovery after insect damage. NPP in the southeastern United States increased in the absence of climate variation. Much of this region consists of aggressively managed forests, with young stand ages and intensive silviculture resulting in increased NPP. The high latitudes of western Canada and Alaska experienced spring warming that could have increased NPP in late spring or early summer.
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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 | 0.001 | 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".