Satellite-Based Modeling of the Carbon Fluxes in Mature Black Spruce Forests in Alaska: A Synthesis of the Eddy Covariance Data and Satellite Remote Sensing Data
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
Abstract Scaling up of observed point data to estimate regional carbon fluxes is an important issue in the context of the global terrestrial carbon cycle. In this study, the authors proposed a new model to scale up the eddy covariance data to estimate regional carbon fluxes using satellite-derived data. Gross primary productivity (GPP) and ecosystem respiration (RE) were empirically calculated using the normalized difference vegetation index (NDVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS). First, the model input is evaluated by comparing with the field data, then established and tested the model at the point scale, and then extended it into a regional scale. At the point scale, the empirical model could reproduce the seasonal and interannual variations in the carbon budget of the mature black spruce forests in Alaska and Canada sites, suggesting that seasonality of the NDVI and LST could explain the carbon fluxes and that the model is robust within mature black spruce forests in North America. Regional-scale analysis showed that the total GPP and RE between 2003 and 2006 were 1.76 ± 0.28 and 1.86 ± 0.26 kg CO2 m−2 yr−1, respectively, in mature black spruce forests in Alaska, indicating that these forests were almost carbon neutral. The authors’ model analysis shows that the proposed method is effective in scaling up point observations to estimate the regional-scale carbon budget and that the mature black spruce forests increased in sink strength during spring warming and decreased in sink strength during summer and autumn warming.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it