A 1 km-resolution terrestrial carbon storage data for China from 2001 to 2020: Multi-pool integration under the IPCC accounting standard
Bibliographic record
Abstract
This dataset presents a high-resolution and temporally continuous carbon storage for China’s terrestrial ecosystems, covering the period from 2001 to 2020 at a 1 km spatial resolution. The dataset was developed using an IPCC-consistent accounting framework that integrates four major carbon pools: aboveground biomass, belowground biomass, soil organic carbon, and dead organic matter. Multi-source remote sensing data, national land cover classifications, biomass models, and soil observations were combined to estimate annual carbon storage across five land use types (cropland, forestland, grassland, built-up land, and unused land). Aboveground carbon is estimated based on biomass models, land use area, and carbon content conversion coefficients specific to land use type (Yang et al., 2023; Luo 2014; Piao et al., 2004; Zhao et al., 2024). Belowground carbon is calculated using the ratio of belowground to aboveground biomass (Luo 2014; Piao et al., 2004; Zhao et al., 2024; Mokany et al., 2006; Eggleston et al., 2006). Soil organic carbon is derived from spatially interpolated soil carbon density data combined with land use classification (Liu et al., 2021; Liu et al., 2020; Xu et al., 2019). Regarding dead carbon pool, estimations were made according to land use type following IPCC guidelines (forestland).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| 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) | 0.004 | 0.002 |
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".