Novel Yield Model of Pinus patula Schltdl. & Cham. Growth near the Ecological Limit in Northwestern Peruvian Andes
Bibliographic record
Abstract
Forest plantations with exotic species in the northwestern Peruvian Andes have brought different ecosystem benefits. The wood productivity in this Páramo region is directly related to the great availability of water from abundant rainfall compared to other regions of the semi-arid Andes. To address the lack of information on forest inventories of plantations in the Páramo region, this study used annual growth rings (dendrochronology) to build new models of tree growth and wood productivity and compared 22-year-old Pinus patula plots with thinning and unthinning treatments. Our results show that late thinning, removing 63% of stem density in the 15th year, does not have significant effects on the diameter increase or stand-level productivity. For these plantations, we propose a management rotation of 21 years with a first thinning treatment (35%) at 5 years and a second thinning treatment (50%) at 12 years. Production at 21 years is expected to be between 194.6 m3 ha−1 and 504.6 m3 ha−1 for stands with low and high wood productivity, respectively. Tree-ring studies are potentially useful for monitoring forest plantations and provide an alternative method for forest managers who use allometric equations to predict silvicultural treatments and to propose management guides for plantations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".