The impact of site on tree form, wood properties, and lumber quality of plantation-grown <i>Pinus patula</i>
Bibliographic record
Abstract
Abstract South Africa is a water scarce country with minimal natural forests. Plantation forest species such as the Mexican tropical hard pine, Pinus patula Schiede & Deppe ex Schltdl. & Cham., was introduced into the country to satisfy local fibre demand. Today, the majority of pine plantations in South Africa are found in the Mpumalanga province. The province has diverse growing conditions in relation to climate and soil properties. Sample plots were established in even-aged P. patula plantations and stratified according to soil parent material and altitude. Growing conditions of each sample plot were characterised according to climate and soil properties. A total of 540 sample trees were measured for height, diameter, density, and latewood content. The sample trees were harvested to produce 2690 logs. The logs were milled, and the resultant lumber was graded according to density, dynamic modulus of elasticity, and modulus of rupture. Annual maximum temperature had the most significant impact on tree growth, while rainfall correlated with wood density and latewood content of standing trees. However, annual maximum temperature and tree height had a negative relationship with the recovered lumber properties and dimensional stability. Spring rainfall appeared to be the most important factor influencing lumber twist and this was possibly due to the associated larger juvenile core present in the trees.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".