Reconstructing and Mapping Annual Net Primary Productivity (NPP) Since 1940 Using Tree Rings in Southern Indiana, U.S.
Bibliographic record
Abstract
Abstract Vegetation absorption is one major form of carbon storage. The earliest spatial distribution of the Net Primary Production (NPP), an index to estimate how much carbon is absorbed, could extend back to the 1980s from satellite imagery. Our study reconstructed a time series annual NPP maps in the southern Indiana since 1940 with point‐by‐point regression models and ring‐width index (RWI) from 16 tree‐ring chronologies. Our RWI‐NPP model had a good performance using Random Forest (RF) regression comprehensively considering both normal and dry years. The RWI‐NPP model performance gap between forest and grassland is acceptable. We also found that the tendency (model of the tendency = −0.50) based on the combination of real NPP data and simulated NPP data were opposite to the one (slope = 18.70) only based on real NPP data where the extended data set could correct some bias caused by limited data. There is a huge NPP fluctuation in the recent years (2010–2013) which is highly likely to be caused by the combination of higher frequency of extreme climate events and the intensive land‐use and land‐cover change. We assume that most of the vegetation pixels had the same growing pattern with the plot in Morgan Monroe Flux Tower whose dominant species is ACSH (35.66%) or the plot in Hoot Woods whose dominant species is FRAM (34.41%). This study is novel in the assessment of the spatial distribution patterns of NPP since 1940. We can witness how the NPP changes within the last 70 years.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".