An explicit forest carbon stock model and applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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 it