Influence of Low Frequency Variability on Climate and Carbon Fluxes in a Temperate Pine Forest in Eastern Canada
Bibliographic record
Abstract
Carbon, water and energy exchanges between forests and the atmosphere depend upon seasonal dynamics of both temperature and precipitation, which are influenced by low frequency climate oscillations such as: El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Eastern Pacific Oscillation (EPO) and the Pacific-North American (PNA). This study investigated the influence of climate oscillations on the local climate and carbon fluxes in a 75-year old temperate pine (Pinus strobus L.) forest, near Lake Erie in southern Ontario, Canada. Analyses indicated mean winter temperatures were correlated to NAO, AO and EPO, total winter precipitation was influenced by PNA and AO, while total snowfall was correlated with PNA and ENSO. These impacts influenced carbon dynamics of the forest during the winter and spring seasons. The EPO had a significant inverse correlation with winter and spring carbon fluxes, while the Pacific Decadal Oscillation (PDO) was significantly correlated with winter respiration. In 2012, an extreme warm event linked to climate oscillations raised temperatures and resulted in a large release of carbon from the forest due to higher ecosystem respiration. As low frequency climate oscillations are important drivers of extreme weather events, affecting their intensity, frequency and spatial patterns, they can cause large changes in carbon exchanges in forest ecosystems in the northeastern parts of North America.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".