Genetic parameters of growth and stem quality traits for jack pine second-generation progeny tested in New Brunswick
Bibliographic record
Abstract
Genetic variation and time trends for growth and stem quality traits were evaluated in jack pine (Pinus banksiana Lamb.) second-generation progeny tests. The study was based on a large dataset, involving 275 half-sib families and 17 field trials, planted across New Brunswick, Canada. Growth was indicated by cumulative tree height (HT) measured at trial ages from 5 to 20 years, and stem quality was evaluated on stem straightness (SST) and stem forking (SF). Results indicated that both HT and SST were moderately genetically controlled, with mean individual heritability estimates of 0.14 (range: 0.03–0.30) and 0.16 (range: 0.10–0.25), respectively, which were lower than those estimates from the first-generation progeny tests. Heritability estimates for HT increased from trial ages of 5 to 10 years and then remained stable, whereas for SST, heritability estimates showed a “Λ” shape with the highest value occurring at trial age of 15 years. HT measurements at trial ages of 5 to 10 years were highly predictive of HT growth at 15 to 20 years, and similarly, SST assessment at 10 years correlated well with assessment at 20 years. Estimates of genetic correlation between HT and SST were positively and moderately strong, which contrasted with the moderately negative genetic correlation between the two traits in the first-generation progeny. Heritability estimates for SF were generally low (mean [Formula: see text] = 0.06) and remained constant over time. SF was essentially not genetically correlated with HT or SST. Family-by-site interactions were weak for HT and SST over the trial ages but were strong for SF at trial age of 10 years or older. The potential impact of selection on genetic architecture of growth and stem quality traits in advanced generation jack pine breeding populations and on breeding and selection strategies was discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".