Post hoc blocking to improve heritability and precision of best linear unbiased genetic predictions
Bibliographic record
Abstract
Single-site clonal trials were simulated with a total of 256 clones "planted" in single-tree plots with three different environmental patterns: only patches (PATCH), only gradients (GRAD), and both components (ALL). Several simulated experimental designs were analyzed (a randomized complete block design; incomplete block designs with 4, 8, 16, and 32 incomplete blocks; and a row-column design) and compared with post hoc blocking of the same designs over a randomized complete block. Additionally, two more incomplete block designs (64 and 128 blocks) were superimposed after the fact to examine extremely small blocks. To select the best fit, the performance of the log-likelihood and mean standard error of the difference (SED) were studied and compared with mean individual broad-sense heritability. Improvement in statistical efficiency (or precision) were obtained with little effort using post hoc blocking. The results from post hoc blocking were promising with negligible differences compared with predesigned local control. The post hoc best designs were row-column (for ALL and PATCH) and incomplete block with eight blocks (for GRAD). Also, mean correlation between the true and predicted values (CORR) showed a reduction in efficiency for extremely small blocks, but no reduction in the genetic variance was noted as the size of the block decreased. Both of the criteria for model selection (log-likelihood and SED) showed similar trend to mean CORR, and their use 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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".