Terrestrial net primary productivity A brief history and a new worldwide database
Bibliographic record
Abstract
Consistent data on terrestrial net primary productivity (NPP) are urgently needed to constrain model estimates of carbon fluxes and hence to refine our understanding of ecosystem responses to climate change. The NPP data have been collected in a coordinated manner for the past 30 years, but comprehensive summaries are rare. We report on the development and availability of a global NPP database that is suitable for modeling of the terrestrial carbon cycle at global and regional scales, for validation of remote sensing data, and for other applications. These data were obtained from the literature on ecophysiological field work and from detailed consultation with the scientific community. Data on NPP, biomass, and associated environmental variables are now publicly available for 53 detailed study sites, of which more than half have data for belowground biomass or biomass dynamics. Aboveground NPP ranges from 35 to 2320 g m 2 a 1 (dry matter) and total NPP from 182 to 3538 g m 2 a 1 . Well-known but previously unobtainable compilations of data, such as the "Osnabrück Data Set" and the International Biological Program (IBP) Woodlands Data Set, are also incorporated in this database. Preliminary exploration of relationships between NPP and mean annual precipitation and temperature suggests that the new 53-site data collection, as well as the Osnabrück and IBP data, are all consistent with the historic "Miami" statistical model. These data are available from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) for biogeochemical dynamics (see http://www.daac.ornl.gov/NPP/).Key words: net primary productivity, grasslands, forests, biogeochemical dynamics, global carbon cycle, model validation.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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; both teacher heads agree on what is shown here.
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".