Carbon dioxide and methane fluxes measurement for tundra ecosystem with ponds in High Arctic
Bibliographic record
Abstract
In this study, CO2 and CH4 fluxes were measured in the tundra ecosystem in order to evaluate the potential future sensitivity of the carbon cycle to climate change using chamber systems and eddy covariance methods during summer in 2019 and 2022 in Canada. The study site is located on dry tundra with ponds in high-arctic near Cambridge Bay, Nunavut, Canada (69°7'47.7"N, 105°3'35.3"W). The vegetation cover around the site is mainly covered with dwarf-shrubs, graminoids, and lichens. CO2 and CH4 fluxes were examined to understand the mechanism of the carbon cycle over the tundra ecosystems with the pond. From chamber methods, the variability of net CO2 exchange was more sensitive to grass of wet condition than vegetation of dry condition and the variability and magnitude of CH4 emission near the pond was larger than that of dry condition. The emission CO2 and CH4 fluxes were examined positive relationship at almost bare soil of wet condition and negative relationship at various vegetation at dry condition. Net ecosystem exchange, ecosystem respiration, and gross primary production were measured or calculated using the both methods to investigate the influence of the ecosystem with ponds in the tundra carbon cycle. This study was supported by a National Research Foundation of Korea grant from the Korean government (MSIP) (NRF-2021M1A5A1065679 and NRF-2021R1I1A1A01053870).
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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 | 0.001 | 0.001 |
| 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 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".