CO<sub>2</sub> flux from <i>Acer saccharum</i> logs: sources of variation and the influence of silvicultural treatments
Bibliographic record
Abstract
Several aspects of the forest carbon cycle have not been examined in detail, including sources of variation in carbon dioxide (CO2) emissions from coarse woody material (CWM). To address this knowledge gap, we examined CO2 emissions from Acer saccharum Marshall logs within four harvesting treatments, using closed chambers fitted to the logs. We found that CO2 emissions were highest for logs in small (31.8 ± 20.4 µmol·CO2·m−3·s−1) and large gaps (29.6 ± 24.4 µmol·CO2·m−3·s−1) compared to those in control (13.9 ± 8.3 µmol·CO2·m−3·s−1) and thinned matrix (13.6 ± 8.0 µmol·CO2·m−3·s−1) treatments. CO2 flux rates did not differ between gap sizes, but they increased with temperature, which was higher in the small gap treatment. In addition, two individual logs fitted with multiple closed chambers revealed significant within-log variability in CO2 emissions. On a subset of logs repeatedly sampled throughout the day, we found that log surface temperature generally peaked at midday and was positively correlated with CO2 emissions, although this relationship was weak in one log. This study provides insight into sources of variation in CO2 emissions from CWM while improving our understanding of the forest carbon cycle.
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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 | 0.001 | 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".