Quantifying Scaling Effect on Gross Primary Productivity Estimation in the Upscaling Process of Surface Heterogeneity
Bibliographic record
Abstract
Abstract Accurate estimation of gross primary productivity (GPP) is essential for understanding the terrestrial carbon budget. Current large‐scale GPP estimates are often obtained at coarse resolutions without considering the subpixel heterogeneity, leading to scaling errors in results. Here, to further characterize (a) the critical sub‐upscaling process causing the largest error and (b) the contributions of various heterogeneity factors in causing the scaling errors, a hydrology‐vegetation model was used to estimate GPP at the 30 m resolution (assumed as reality), and other coarser resolutions (60, 120, 240, 480, and 960 m, assumed as approximations) for 16 mountainous watersheds. Then, GPP scaling errors in the upscaling process of surface heterogeneity were investigated by the root mean squared error between the reality and approximations. Results showed that any surface heterogeneity aggregation from fine to coarse resolutions (e.g., 30–960 m) could cause GPP scaling errors (133 ± 40 gCm−2yr−1), and the aggregation from medium to coarse resolutions (e.g., 240–960 m) may be the largest source. More specifically, GPP scaling errors caused by the vegetation heterogeneity aggregation from fine to medium resolutions were relatively small, and the GPP errors caused by the surface topography aggregation from fine to coarse resolutions were all non‐negligible. Elevation aggregation caused larger GPP scaling error than the aggregations of land cover, leaf area index, slope, and aspect. This work highlights the need to consider surface heterogeneity (especially the elevation information) when modeling mountain vegetation GPP at coarse resolutions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".