The impact of climate on the dominant height and climate thresholds for <i>P. elliottii, P. taeda, P. patula</i>, and <i>P. patula x P. tecunumanii</i> plantation forests
Bibliographic record
Abstract
Plantation forests were introduced into South Africa to satisfy the regional demand for wood-based products, which are largely found in the Mpumalanga province. To better understand the impacts of climate on the dominant height growth of commercially important plantation species, enumeration data collected from 2012 to 2023 was scrutinised, representing 34740 plots in 2082 plantation compartments. Statistically significant multiple linear regression models were developed to predict dominant heights for Pinus elliottii, Pinus taeda, Pinus patula, and Pinus patula x Pinus tecunumanii hybrids using climate variables as independent factors. Mean annual maximum temperature was statistically significant when modelling dominant height for P. elliottii, while winter maximum temperature was significant for P . taeda, P. patula, and P. patula x P. tecunumanii. Rainfall was found to be significant for P. elliottii and P. taeda, while spring rainfall was found to be more important when modelling dominant height for P. patula. Interestingly, autumn rainfall was found to have a negative impact on dominant height growth of P. taeda and P. patula, while rainfall was not significant in P. patula x P. tecunumanii. The findings imply that the southern pines have water thresholds, while Mexican pines are more temperature limited than water limited.
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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 | 0.001 | 0.000 |
| 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".