Comparison of regional carbon flux estimates from CO<sub>2</sub> concentration measurements and remote sensing based footprint integration
Bibliographic record
Abstract
Quantification of terrestrial CO 2 sources and sinks at regional scales (∼10 2 –10 6 km 2 ) is fundamental to improving our understanding of the terrestrial carbon cycle. Two independent methods to extract the gross primary productivity (GPP) from atmospheric CO 2 concentration measurements were explored and compared in this study. The methods are (1) planetary boundary layer (PBL) carbon budget analysis that allows the estimation of regional GPP at daily time steps from hourly CO 2 concentration measurements and (2) spatially explicit hourly carbon cycle modeling based on remote sensing and then integrating the daily flux field with a concentration footprint function depending on wind and stability. These methods have been applied to a 28‐m tower at an old black spruce site near Candle Lake (∼100 km NE of Prince Albert: 53.98717°N, 105.11779°W). The estimates of daily GPP by these two approaches agreed well for 2003 (slope = 0.99; r 2 = 0.89). In order to test these methods of inferring the regional GPP from mixing ratio measurements, we also compared the estimates of regional GPP with estimates made using eddy covariance (EC) flux measurements, although their respective source areas are different. They had similar seasonal patterns, but the regional estimates were consistently smaller than the local EC flux derived GPP throughout the growing season in 2003. These estimates of annual regional GPP were 649–664 g C m −2 for 2003 while the EC‐derived annual GPP was 819–847 g C m −2 . The annual difference was about 20–25%. The EC flux footprint of the tower was relatively homogeneous old black spruce while the concentration footprint, which was a few orders of magnitude larger than the flux footprint, covered boreal evergreen and deciduous broadleaf forests, grassland, cropland, and lakes. Nonforested land occupied about 10–50% of the concentration footprint depending on wind direction and speed and was less productive than the black spruce forest. The discrepancies between regional and local GPP estimates reflected the differences in underlying land surfaces represented by the different footprint areas.
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How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".