A Practical Algorithm for Correcting Topographical Effects on Global GPP Products
Bibliographic record
Abstract
Abstract Vegetation in mountainous areas contributes about 36% to the global gross primary productivity (GPP). However, the influences of topography on radiation and water redistributions in mountain ecosystems are so far ignored in existing global GPP data sets. Here, an eco‐hydrological model was adopted to simulate 30 m resolution mountain and flat GPP over 16 watersheds. Then, a topographical correction index (TCI) was developed based on simulated soil water redistribution (TCIwater), radiation redistribution (TCIrad), and redistribution of climate factors (TCIclim). Finally, the proposed TCI was applied to four GPP data sets. The mean‐bias‐error (MBE), determination coefficient (R2), and Root‐Mean‐Square‐Error (RMSE) between mountain GPP and flat GPP (or GPP data sets) were used for evaluation. Results showed that the MBE of flat GPP before correction (194 g C m−2 yr−1) was reduced to 126, 94, and 2 g C m−2 yr−1 after the corrections of TCIwater, TCIrad, and TCIclim, highlighting the effectiveness of integrated redistribution information in correcting the topographical effect on GPP estimation. The relationship between mountain and flat GPP after the TCI correction was improved at the 30 m resolution (increasing R2 by 0.09 and reducing RMSE by 90 g C m−2 yr−1) and 480 m resolution (increasing R2 by 0.13 and reducing RMSE by 178 g C m−2 yr−1). Regarding the four GPP data sets after the TCI correction, the MBE of 183 g C m−2 yr−1 was averagely reduced to 17 g C m−2 yr−1, and RMSE was reduced by 118 g C m−2 yr−1 at 480 m resolution. This study suggests that integrating topography‐induced interactions into current GPP data sets is a feasible way to understand the carbon budget in mountain ecosystems.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".