Carbon stocks across a chronosequence of thinned and unmanaged red pine (<i>Pinus resinosa</i>) stands
Bibliographic record
Abstract
Forests function as a major global C sink, and forest management strategies that maximize C stocks offer one possible means of mitigating the impacts of increasing anthropogenic CO2 emissions. We studied the effects of thinning, a common management technique in many forest types, on age-related trends in C stocks using a chronosequence of thinned and unmanaged red pine (Pinus resinosa) stands ranging from 9 to 306 years old. Live tree C stocks increased with age to a maximum near the middle of the chronosequence in unmanaged stands, and increased across the entire chronosequence in thinned stands. C in live understory vegetation and C in the mineral soil each declined rapidly with age in young stands but changed relatively little in middle-aged to older stands regardless of management. Forest floor C stocks increased with age in unmanaged stands, but forest floor C decreased with age after the onset of thinning around age 40 in thinned stands. Deadwood C was highly variable, but decreased with age in thinned stands. Total ecosystem C increased with stand age until approaching an asymptote around age 150. The increase in total ecosystem C was paralleled by an age-related increase in total aboveground C, but relatively little change in total belowground C. Thinning had surprisingly little impact on total ecosystem C stocks, but it did modestly alter age-related trends in total ecosystem C allocation between aboveground and belowground pools. In addition to characterizing the subtle differences in C dynamics between thinned and unmanaged stands, these results suggest that C accrual in red pine stands continues well beyond the 60-100 year management rotations typical for this system. Management plans that incorporate longer rotations and thinning in some stands could play an important role in maximizing C stocks in red pine forests while meeting other objectives including timber extraction, biodiversity conservation, restoration, and fuel reduction goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".