Competition effects in breeding value prediction of forest trees
Bibliographic record
Abstract
Competition is a concern for tree breeding because of its potential to reduce the genetic gain. Competition, if not accounted for in the analytical model, can potentially introduce a source of bias in genetic parameter estimation and breeding value prediction. This study modelled competition between trees in 20 Swedish progeny trials of Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and lodgepole pine (Pinus contorta Douglas ex Loudon). The competition model assumed a tree has a direct additive genetic effect, which affects the tree’s own phenotype, and an indirect additive effect, which affects the phenotypes of its neighbours. Genetic parameters were estimated via a factor analytic structure in which separate indirect effects were considered for each neighbour, or via a combined indirect effect approach. We analysed diameter, as it is the trait that can be expected to be affected most by competition. Competition at the genetic level was detected in 17 of the 20 trials analysed. In most cases, the ratio of indirect to direct additive variance was less than 20% and no major changes in ranking resulted. At this stage, there is little incentive to incorporate indirect effects into program-wide genetic evaluation models. The added complexity is not commensurate with the benefit that would be gained.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".