The Polar Vegetation Photosynthesis and Respiration Model: a parsimonious, satellite-data-driven model of high-latitude CO <sub>2</sub> exchange
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. We introduce the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM), a remote-sensing-based approach for generating accurate, high-resolution (≥ 1 km2, 3 hourly) estimates of net ecosystem CO2 exchange (NEE). PolarVPRM simulates NEE using polar-specific vegetation classes, and by representing high-latitude influences on NEE, such as the influence of soil temperature on subnivean respiration. We present a description, validation and error analysis (first-order Taylor expansion) of PolarVPRM, followed by an examination of per-pixel trends (2001–2012) in model output for the North American terrestrial region north of 55° N. PolarVPRM was validated against eddy covariance (EC) observations from nine North American sites, of which three were used in model calibration. Comparisons of EC NEE to NEE from three models indicated that PolarVPRM displayed similar or better statistical agreement with eddy covariance observations than existing models showed. Trend analysis (2001–2012) indicated that warming air temperatures and drought stress in forests increased growing season rates of respiration, and decreased rates of net carbon uptake by vegetation when air temperatures exceeded optimal temperatures for photosynthesis. Concurrent increases in growing season length at Arctic tundra sites allowed for increases in photosynthetic uptake over time by tundra vegetation. PolarVPRM estimated that the North American high-latitude region changed from a carbon source (2001–2004) to a carbon sink (2005–2010) to again a source (2011–2012) in response to changing environmental conditions.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| 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 it