Bibliographic record
Abstract
As a graphical data analysis tool, biplot analysis has increasingly been used in analyzing genotype × environment data and other types of two‐way data. One limitation of biplot analysis is that it requires a complete two‐way table. This paper reports on a procedure for estimating missing values in a two‐way table so that incomplete data can be effectively analyzed using biplots. This procedure involves iteration of missing values based on singular value decomposition (SVD), which is the basic technique for biplot analysis. Simulation indicates that the proposed procedure successfully predicts missing values and recovers patterns for two sample datasets. On a smaller wheat ( Triticum aestivum L.) dataset, the estimation was successful only when the proportion of missing data was less than 40%; for a larger oat ( Avena sativa L.) dataset, the estimation was successful even when 60% of the data were treated as missing. The use of the SVD‐based missing‐value‐estimation procedure enabled incomplete multiple‐year data to be effectively analyzed in a single biplot. As a result, genotypes not tested in the same environments can be reasonably compared, and genotypes that have not been fully tested can be critically evaluated.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".