A framework for testing radiata pine under projected climate change in Australia and New Zealand
Bibliographic record
Abstract
Radiata pine plantation resources in Australia and New Zealand are a highly productive source of solid-wood and pulp products for domestic consumption and export. This has largely been achieved through long-term investments in tree breeding programs that select the best-performing genotypes for varied regional environments. However, climate change could threaten the realisation of genetic improvement in plantations due to suboptimal matching of improved planting stock to new climate conditions. Here, we investigate how information from genetic field tests could be utilised under anticipated climate change. We use principal component analysis and Mahalanobis distance measures to find the closest match between climate of plantation regions in the future and current climate of field test sites. By 2050, future climates of some important plantation regions are expected to match climates currently present in different regions. For example, future climates of Green Triangle, a key plantation region in Australia, will better match current climate of Western Australia. The Central North Island of New Zealand will shift to warmer and wetter climate with no current analogue, and Western Australia, to warmer and drier no-analogue climate. The latter is also likely to fall outside the climate niche where radiata pine can be grown in the future. Nevertheless, for the majority of radiata pine plantation regions in Australia and New Zealand our analysis provides a framework of how anticipated climate change can be addressed in tree improvement programs using existing field tests.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".