Production efficiency of loblolly pine stands under roundwood and carbon price risks
Bibliographic record
Abstract
This study examines the efficiency of loblolly pine ( Pinus taeda L.) production under roundwood and carbon price risks. The data are generated from the biophysical–economic optimization model, consisting of the loblolly pine growth and yield model in Georgia, United States, combined with a stochastic economic model. The model incorporates the timber and carbon price risk parameters and generates the optimal biomass volumes and the associated harvest profits for 56 scenarios given different silvicultural treatments and price risks. Timber production efficiencies under each scenario are evaluated using the data envelopment analysis. This study also assesses potential economic losses due to inefficient forest production. The result shows that forest landowners with lower risk tolerance have a higher profit foregone. Inefficient forest management could cause up to $319/ha and $405/ha of potential economic losses under herbicide and fertilizer treatment scenarios, respectively. As timber-related price risks can influence forest landowners’ decisions, the findings of this study incorporating different risks would help forestry professionals and policymakers to establish a more realistic and greater degree of accuracy in the forest productivity evaluation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".