Interannual variability in the atmospheric CO<sub>2</sub> rectification over a boreal forest region
Bibliographic record
Abstract
Ecosystem CO2 exchange with the atmosphere and the planetary boundary layer (PBL) dynamics are correlated diurnally and seasonally. The strength of this kind of covariation is quantified as the rectifier effect, and it affects the vertical gradient of CO2 and thus the global CO2 distribution pattern. An 11‐year (1990–1996, 1999–2002), continuous CO2 record from Fraserdale, Ontario (49°52′29.9″N, 81°34′12.3″W), along with a coupled vertical diffusion scheme (VDS) and ecosystem model named Boreal Ecosystem Productivity Simulator (BEPS), are used to investigate the interannual variability of the rectifier effect over a boreal forest region. The coupled model performed well (r2 = 0.70 and 0.87, at 40 m at hourly and daily time steps, respectively) in simulating CO2 vertical diffusion processes. The simulated annual atmospheric rectifier effect varies from 3.99 to 5.52 ppm, while the diurnal rectifying effect accounted for about a quarter of the annual total (22.8∼28.9%).The atmospheric rectification of CO2 is not simply influenced by terrestrial source and sink strengths, but by seasonal and diurnal variations in the land CO2 flux and their interaction with PBL dynamics. Air temperature and moisture are found to be the dominant climatic factors controlling the rectifier effect. The annual rectifier effect is highly correlated with annual mean temperature (r2 = 0.84), while annual mean air relative humidity can explain 51% of the interannual variation in rectification. Seasonal rectifier effect is also found to be more sensitive to climate variability than diurnal rectifier effect.
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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 | 0.000 |
| 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 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".