Modeling unstructured heterogeneity along with spatially correlated errors in field trials
Bibliographic record
Abstract
In this paper we consider analysis of two experimental data sets for evaluating lentil genotypes. One of these data sets comes from an incomplete block design and the other one from a complete block design. The incomplete blocks contribute to the experimental error reduction and spatially correlated plot-errors can be modeled using autoregressive scheme that may lead to further improvement in the assessment of the genotypes. Such an approach was applied in several other studies to model the linear trends and spatially correlated errors. However, the assumption of a constant error variance restricts the scope of the analysis in many agricultural field trials, and in other situations in general, where heterogeneity of error variances is a reality. In this study, we have approached the problem first by fitting a model with constant error variance and generating the residuals. Using the squared residuals, we use K-cluster means technique to group the experimental units for similar squared-residuals. Next, we allow the error variances to vary with the group of the experimental units which need not require any spatial restrictions to model the error variances. The number of heterogeneous errors and the experimental units belonging to the heterogeneous clusters are obtained using the AIC criterion values followed by a groups merger scheme based on insignificant change in the residual maximum log likelihood values. The final models with heterogeneous variances were used to evaluate the precision of the genotype means comparisons. We found a substantial improvement on the effciency of the pair-wise comparisons over the other ways of analysis. We recommend the application of this procedure in any general situation permitting unstructured heterogeneity.
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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 | 0.038 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".