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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".