Carbon pools of managed and unmanaged stands of ponderosa and lodgepole pine forests in Wyoming
Bibliographic record
Abstract
Forest management practices can have a significant effect on above- and below-ground carbon (C) pools. To better understand the distribution of forest C pools, we evaluated representative forest stands within two dominant Wyoming forest types to assess differences resulting from management practices that have occurred over several decades. Study sites included four ponderosa pine ( Pinus ponderosa Douglas ex Lawson & C.Lawson) treatments (100-year-old unmanaged, 46-year-old even-aged, 110-year-old uneven-aged, and 90-year-old intensively harvested) and two lodgepole pine ( Pinus contorta Engelm. var. latifolia (Engelm. ex Wats.) Critchfield) treatments (145-year-old unmanaged and 45-year-old even-aged). Comparisons of aboveground C pools revealed that distributions of live and dead biomass C pools were different between unmanaged and managed stands; however, belowground soil C pools were similar among stands within the two forest types. Overall, unmanaged stands of both forest types had higher total ecosystem C pools (249 and 247 Mg C·ha–1for ponderosa and lodgepole pine, respectively) compared with managed stands (ponderosa pine: even-aged, 164 Mg C·ha–1; uneven-aged, 170 Mg C·ha–1; intensively harvested, 200 Mg C·ha–1; and lodgepole pine even-aged, 117 Mg C·ha–1). Our results indicate timber harvesting has a major influence on total ecosystem C pools by reducing live tree biomass.
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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 | 0.000 | 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) | 0.001 | 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".