Achieving higher heritabilities through improved design and analysis of clonal trials
Bibliographic record
Abstract
Clonal testing was studied under different environmental patterns and experimental designs through simulation with the criteria of maximizing broad-sense heritability estimates and genetic gain from clonal selection. Several experimental designs were studied together with three patterns of environmental variability. In addition, empirical 95% confidence intervals for heritability estimates were compared with Dickerson's approximate method. Other elements studied included (i) conditions under which different environmental patterns yield high or low heritabilities and (ii) effects of varying the number of ramets per clone. Row-column designs produced the highest mean individual broad- sense heritability, but these designs were only slightly more efficient than incomplete block designs with small block sizes. For all experimental designs, Dickerson's approximate method for estimating the variance of heritability estimates produced reasonable 95% confidence intervals but overestimated the upper confidence limit of complex designs. Larger heritabilities were found with higher tree-to-tree spatial correlations and lower amounts of microsite residual variance, and varying gradients had negligible influence. The effect of implementing Latinization was significant on increasing heritability, but small in practical terms, and was more important for patchy surfaces. Experiments with more ramets per clone yielded higher clonal mean heritabilities, and using between four and six ramets per clone per site is recommended.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".