Performance differences in <i>Pinus radiata</i> progeny with differing site nutrient availability
Bibliographic record
Abstract
Two intensive harvesting trials with contrasting nutrient capital were examined for genetic × environment interactions to age 5 years after planting. Treatments included differences in removal of organic matter and in site preparation and weed control, with each treatment having both fertilized and nonfertilized plots. Three harvest treatments (both fertilized and nonfertilized) were common to both sites, with two additional treatments at one site. There were four replicate plots of each treatment combination at each site, with two trees from each of three control seed lots and 47 open-pollinated families chosen to represent the range of performance for growth planted in each plot. Large differences among sites and among treatments in both growth and foliar nutrient concentration were observed. Genetic × site interactions and genetic × treatment within site interactions were seldom significant. Significant interactions did not appear to be related to changes in rankings of families, but rather to the differences in variance among families in different treatments. This study suggests that selection of specific radiata pine (Pinus radiata D. Don) families for better growth performance on nutrient-deficient sites in New Zealand would not result in substantial improvement over selection for growth on all sites disregarding nutrient availability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".