Quantification of realised genetic gain in radiata pine and its incorporation into growth and yield modelling systems
Bibliographic record
Abstract
Realised genetic gain for radiata pine (Pinus radiata D. Don) was estimated using data from 46 installations of three series of block-plot trials spanning a wide range of site types throughout New Zealand. These trials contained 63 unique seedlots with different levels of genetic improvement. Realised genetic gain was quantified using two measures of productivity: site index and 300 Index (a measure of volume productivity). The level of genetic improvement of each seedlot was determined by its GF Plus rating, a genetic rating system based on breeding values used for New Zealand radiata pine. There was a positive relationship between GF Plus rating and both productivity measures. Differences of 25% in total standing volume at age 30 years and of 5.6% in site index were found between unimproved (GF Plus 9.9) and highly improved (GF Plus 25) seedlots. Each unit increase in GF Plus rating was associated with a 1.51% increase in volume growth rate. In absolute terms, the magnitude of the increase was greater on more productive sites compared with less productive sites, although in percentage terms, it varied little between sites or regions. Quantification of genetic gain in this manner enables it to be easily incorporated into existing growth and yield simulators.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".