A Strategy for the Second Breeding Cycle of Larix x marschlinsii in Québec, Canada Including Experiments to Guide Interspecific Tree Breeding Programme
Bibliographic record
Abstract
Abstract A strategy for the genetic improvement of Larix decidua P. Mill., L. kaempferi (Lamb.) Carrière and their hybrid (L. x marschlinsii Coaz) in Québec, Canada, was set up to provide short and long-term genetic gains, as well as basic populations for some fundamental experiments. A reciprocal recurrent selection with forward selection strategy (RRS-FS) will be applied by using a pollen mix breeding with a partial population paternity analysis (PMX/WPPA). The genetic mechanisms responsible for heterosis among trees remain poorly understood. This complicates the implementation of multi-species genetic improvement programmes seeking to achieve the full benefits of interspecific hybridization. Various strategies to exploit heterosis are outlined. To expand our knowledge and guide our future choices, basic research has been integrated directly into breeding and testing activities, as well as through experiments integrated within activities leading to recommendations about the top-ranked families to be used in reforestation (cuttings). These experiments can also serve as the basis for future studies seeking a better understanding of heterosis through molecular genetics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".