Retrospective genetic testing of<i>Picea abies</i>under controlled temperature and moisture regimes
Bibliographic record
Abstract
A retrospective early test of Picea abies (L.) Karst. was conducted under two temperature ("high" and "low") and two irrigation ("well-watered" and "drought") regimes. Height, dry mass, and phenological traits were assessed for seedlings from 36 open-pollinated families grown in a growth chamber for two growth periods. The attributes measured were compared with the growth traits of three 24-year-old field progeny trials propagated from the same parents. Heritabilities for biomass and height were mainly moderate to strong (0.10.8) in the well-watered treatments, while they were weaker in the drought treatments (0.00.5). Juvenile-mature (JM) genetic correlations for growth traits were generally weak. There were, on average, stronger JM correlations in the drought treatments than in the well-watered treatments. Similarly, there were stronger JM correlations in the high- than in the low-temperature treatment. The results suggest that genotype × environment (G×E) interaction between the juvenile and mature environment is one of the reasons for low JM correlations. This supports the hypothesis that higher JM correlations can be obtained by mimicking natural growth-limiting factors in the juveniles' growth chambers. We conclude that further development of more efficient early selection methods for P. abies should include periodic drought and the development of optimal temperature regimes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".