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Record W4312125630 · doi:10.3390/f13122109

Novel Yield Model of Pinus patula Schltdl. & Cham. Growth near the Ecological Limit in Northwestern Peruvian Andes

2022· article· en· W4312125630 on OpenAlexaff
Daigard Ricardo Ortega Rodríguez, Andrea Hevia, Raúl Sánchez‐Salguero, Santiago Bermudez Dobbertin, Jedi Rosero-Alvarado, Manuel Chavesta, Mário Tomazello-Filho

Bibliographic record

VenueForests · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversité LavalKruger (Canada)
FundersUniversidad Nacional Agraria La Molina
KeywordsPinus patulaThinningProductivityAgroforestryForest managementForestryBasal areaSilvicultureTree allometryEnvironmental scienceGeographyEcologyBiologyBiomass (ecology)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.025
GPT teacher head0.219
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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