Interannual Variability of Summer Net Ecosystem CO<sub>2</sub> Exchange in High Arctic Tundra
Bibliographic record
Abstract
Abstract Arctic terrestrial ecosystems may be contributing to increasing atmospheric carbon dioxide (CO2) concentrations under amplified climate change at high‐latitudes. This research investigates how summer net ecosystem CO2 exchange (NEE) and its component fluxes, gross primary production (GPP), and ecosystem respiration (Reco) varied over five years (2008, 2009, 2010, 2012, and 2014) at the Cape Bounty Arctic Watershed Observatory (74.92°N, 109.58°W). The eddy covariance technique was used to measure NEE and a combined light and temperature response model was used to partition NEE into GPP and Reco. Total summer NEE varied from −19.8 to 7.9 g C m−2. Despite two summers with net CO2 uptake, this tundra was likely a source of CO2 on an annual basis. In most years, growing degree days with a base 0°C (GDD0) had more predictive power than other environmental variables in random forest analyses of daily NEE. Interannual variability in total summer NEE over the five study years was also attributed to greater variability in GPP than Reco (coefficient of variation: 62% and 27%, respectively). However, total summer GDD0 significantly correlated with total summer Reco but not NEE nor GPP. Instead, summer total NEE and GPP significantly correlated with satellite‐derived normalized difference vegetation index (NDVI), which may have exhibited carry‐over effects from one year to the next to limit the impact of current year GDD0 on total summer NEE at this tundra site.
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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 | 0.001 |
| 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".