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Record W4311881546 · doi:10.1515/hf-2022-0126

The impact of soil, altitude, and climate on tree form and wood properties of plantation grown <i>Pinus patula</i> in Mpumalanga, South Africa

2022· article· en· W4311881546 on OpenAlexaff
Jaco-Pierre van der Merwe, Ilaria Germishuizen, Charlie Clarke, Shawn D. Mansfield

Bibliographic record

VenueHolzforschung · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPinus patulaForestrySoftwoodEnvironmental scienceAltitude (triangle)AgroforestryGeographyBotanyBiologyMathematics

Abstract

fetched live from OpenAlex

Abstract Plantation forests were originally established in South Africa to meet an increasing demand for solid wood products as there was a limited supply from native forests. The majority of the commercial softwood plantations were established with Mexican Pinus patula . Since growing conditions are known to impact tree growth, tree form, and wood quality of P. patula , sample plots were established over a cross-section of plantations in the Lowveld Escarpment and Highveld forestry regions of South Africa that covered an array of geologies and altitudes. Each sample plot was classified according to soil properties, rainfall, and temperature, and trees within the plots were measured for growth, form, and wood properties. Soil, growing days, and temperature were found to have little impact on tree form and wood properties. However, rainfall and specifically, spring rainfall, was found to have a highly significant impact on late wood formation, proportion of juvenile core, and wood density. In addition, tree height was found to be strongly correlated with maximum annual temperature.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.192

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2022
Admission routes1
Has abstractyes

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