An explicit forest carbon stock model and applications
Bibliographic record
Abstract
How to achieve reliable monitoring of global forest carbon sinks is of great urgency, and the combination of remote sensing and ground observation has become a hot topic. The relationship between remote sensing features (vegetation indices, spectral, textural, backscattering coefficients) and forest carbon stock is still unclear, hence this paper proposes a pixel-level, multi-scale, high-precision Explicit Forest carbon stock Model (EFM) that is universal and adaptive. First, the pixel size, forest canopy density, terrain slope, and forest height were used in the construction of EFM; Second, the EFM parameters were solved by simulated forest scene; Third, the EFM was used in simulated and real forest scenes to verify the accuracy, robustness, and applicability, the experiments show that the relative error is about 15%; Finally, the first time mapping forest carbon stock over 200,000 km2 area at 2 m scale was completed by the EFM. The EFM convert the calculation unit from individual tree to pixel compared with allometric growth equation, and overcome the poor universality of regression inversion methods, which can be used to monitor forest carbon dynamics at global scale.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".