Carbon dioxide exchange in three tundra sites show a dissimilar response to environmental variables
Bibliographic record
Abstract
Due to the heterogeneity of the Arctic tundra, general current understanding of net carbon (C) uptake in these ecosystems is poorly developed. This study investigates the dependency of carbon dioxide (CO2) fluxes on environmental, meteorological and vegetation properties in high, low and subarctic tundra sites for the purpose of exposing the environmental, meteorological and vegetation factors, especially season length, that drive CO2 fluxes in disparate tundra environments. Partitioning CO2 fluxes and redefining seasons in the same manner improved our interpretation of the factors affecting flux variability. An improved understanding of the control of ancillary variables on net ecosystem exchange (NEE), gross primary production (GPP) and ecosystem respiration (Re) will improve the accuracy with which CO2 exchange seasonality in Arctic tundra ecosystems is modelled. Fluxes were measured with the eddy covariance technique at three Arctic tundra sites in Greenland (74.47 °N), Canada (64.52 °N) and Sweden (68.33 °N). Our results show that the variations in CO2 fluxes were driven by varying factors at each site with the growing season length controlling at least one flux component at Zackenberg and Daring Lake. Growing season NEE correlated mainly to cumulative radiation and temperature-related variables at Zackenberg, while at Daring Lake the same variables showed significant correlations with the partitioned fluxes (GPP and Re). Stordalen was temperature dependent during the growing season. This study emphasizes the inherent need for a standardized year round measurement and analytical routine of CO2 fluxes and ancillary variables, and investigations into the interconnectedness of ancillary variables in the Arctic tundra.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".