Relating nutritional and physiological characteristics to growth of Pinus radiata clones planted on a range of sites in New Zealand
Bibliographic record
Abstract
Six clones of radiata pine with known differences in growth rate were examined for clonal nutritional characteristics and for physiological determinants of clonal growth rate. We compared growth, foliar characteristics and nutrient, ¹³C and ¹⁵N concentration data for the six clones in 4- to 6-year-old field trials planted over a range of nutritionally contrasting sites. These data were also compared with growth, nutrient uptake and remobilization, foliar characteristic and gas exchange data from intensive physiological glasshouse experiments using 1- and 2-year-old plants of the same clones. Significant genotype x environment interactions in our field experiments conducted over strong nutritional gradients allowed us to identify radiata pine clones with consistent, superior growth and nutritional characteristics and clones that may be suited to particular site conditions. Our results suggest that the opportunity exists to exploit clone x site variation for site-specific clonal deployment and planting of fast-growing clones could be accompanied by planting of clones able to take relative advantage of site nutritional characteristics. Faster tree growth was not strongly related to any physiological characteristic, and the factors influencing growth rate differed among clones. The fastest-growing clone had consistent, high uptake of all nutrients, high fascicle weights and high water-use efficiency.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".