Assessing Spatiotemporal Variation of Forest Aboveground Carbon Sequestration Coupling Landscape Models With Remote Sensing Datasets In Western Sichuan, China
Bibliographic record
Abstract
The spatial variation of forest aboveground carbon sequestration (ACS) is crucial for assessing the carbon storage and time of carbon-emission peaking. Unfortunately, a systematic assessment of accurate ACS and its change has not yet been developed, especially at a large scale. Here, we evaluate the spatiotemporal ACS trend based on LANDIS PRO and PnET-II models from 2000 to 2020 in Western Sichuan, China, coupled with multi-source remote sensing datasets (e.g., MODIS and Lansat) and in situ forest inventory to obtain the landscape information. The Mann-Kendall trend test is used to examine the ACS trend. The total ACS increased from 344.62 to 466.99 Tg due to large-scale reforestation programs from 2000 to 2020. The evergreen coniferous forest contributed the most ACS (85.28%) in Western Sichuan compared with other species. The developed methodology can be applied to analyzing the ACS at the watershed and regional levels promptly and helping decision-makers and managers develop effective forest management measures.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".