Effects of silvicultural practices on carbon stores in Douglas-fir western hemlock forests in the Pacific Northwest, U.S.A.: results from a simulation model
Bibliographic record
Abstract
We used a new model, STANDCARB, to examine effects of various treatments on carbon (C) pools in the Pacific Northwest forest sector. Simulation experiments, with five replicates of each treatment, were used to investigate the effects of initial conditions, tree establishment rates, rotation length, tree utilization level, and slash burning on ecosystem and forest products C stores. The forest examined was typical of the Cascades of Oregon and dominated by Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and western hemlock (Tsuga heterophylla (Raf.) Sarg). Simulations were run until a C steady state was reached at the landscape level, and results were rescaled relative to the potential maximum C stored in a landscape. Simulation experiments indicated agricultural fields stored the least (15% of the maximum) and forests protected from fire stored the greatest amount (93% of the maximum) of landscape-level C. Conversion of old-growth forests to any other management or disturbance regime resulted in a net loss of C, whereas conversion of agricultural systems to forest systems had the opposite effect. The three factors, in order of increasing importance, most crucial in developing an optimum C storage system were (i) rotation length, (ii) amount of live mass harvested, and (iii) amount of detritus removed by slash burning. Carbon stores increased as rotation length increased but decreased as fraction of trees harvested and detritus removed increased. Simulations indicate partial harvest and minimal fire use may provide as many forest products as the traditional clearcut broadcast-burn system while increasing C stores. Therefore, an adequate supply of wood products may not be incompatible with a system that increases C stores.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".