Soil surface CO<sub>2</sub> flux in a boreal black spruce fire chronosequence
Bibliographic record
Abstract
Understanding the effects of wildfire on the carbon (C) cycle of boreal forests is essential to quantifying the role of boreal forests in the global carbon cycle. Soil surface CO 2 flux (R s ), the second largest C flux in boreal forests, is directly and indirectly affected by fire and is hypothesized to change during forest succession following fire. The overall objective of this study was to measure and model R s for a black spruce ( Picea mariana [Mill.] BSP) postfire chronosequence in northern Manitoba, Canada. The experiment design was a nested factorial that included two soil drainage classes (well and poorly drained) × seven postfire aged stands. Specific objectives were (1) to quantify the relationship between R s and soil temperature for different aged boreal black spruce forests in well‐drained and poorly drained soil conditions, (2) to examine R s dynamics along postfire successional stands, and (3) to estimate annual soil surface CO 2 flux for these ecosystems. Soil surface CO 2 flux was significantly affected by soil drainage class (p = 0.014) and stand age (p = 0.006). Soil surface CO 2 flux was positively correlated to soil temperature (R 2 = 0.78, p < 0.001), but different models were required for each drainage class × aged stand combination. Soil surface CO 2 flux was significantly greater at the well‐drained than the poorly drained stands (p = 0.007) during growing season. Annual soil surface CO 2 flux for the 1998, 1995, 1989, 1981, 1964, 1930, and 1870 burned stands averaged 226, 412, 357, 413, 350, 274, and 244 g C m ‐2 yr −1 in the well‐drained stands and 146, 380, 300, 303, 256, 233, and 264 g C m −2 yr −1 in the poorly drained stands. Soil surface CO 2 flux during the winter (from 1 November to 30 April) comprised from 5 to 19% of the total annual R s . We speculate that the smaller soil surface CO 2 flux in the recently burned than the older stands is mainly caused by decreased root respiration.
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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 | 0.000 |
| 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".