Carbon Cycling in Canada’s Boreal Forest: Investigating the Role of Vegetation Composition on Net Ecosystem Exchange
Bibliographic record
Abstract
Canada’s boreal forest plays an important role in the global carbon (C) cycle, but its C dynamics are being altered by climate change. Shifts in temperature, precipitation, and growing season patterns influence how these ecosystems store and release C. Understanding how different regions of Canada’s boreal forest regulate C and respond to climate variability is essential for predicting the impacts of climate change on these ecosystems. My study examines how vegetation composition influences C cycling by comparing the net ecosystem exchange (NEE) at a Boreal Mixedwood Forest in Ontario, and an Eastern Boreal, Mature Black Spruce Forest in Quebec. Using six years (2004-2009) of eddy covariance (EC) measurements, I analyze seasonal and interannual variability in C fluxes and identify environmental drivers influencing these variations. Preliminary results show that the Mixedwood forest consistently functions as a net C sink (-97.97 ± 35.98 g C m-2 y-1), while the Black Spruce site fluctuates between a small C sink and source (-0.2268 ± 15.18 g C m-2 y-1). Seasonal trends reveal that both sites act as C sources in winter when ecosystem respiration (ER) exceeds gross ecosystem production (GEP), and as C sinks in summer, when GEP surpasses ER. However, C uptake is substantially higher at the Mixedwood site, driven by greater GEP and ER throughout the year. My research provides insights into how forest composition regulates boreal C dynamics and enhances our understanding of how these forests may respond to future climate change, informing sustainable forest management strategies.
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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 | 0.002 | 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".