Multivariate analysis of stripe rust assessment and reactions of barley in multi-location nurseries
Bibliographic record
Abstract
Xi, K., Chen, X. M., Capettini, F., Falconi, E., Yang, R. C., Helm, J. H., Holtz, M. D., Juskiw, P., Kumar, K., Nyachiro, J. and Turkington, T. K. 2013. Multivariate analysis of stripe rust assessment and reactions of barley in multi-location nurseries. Can. J. Plant Sci. 93: 209–219. A total of 1357 entries, mainly consisting of hulled two-row, hulled six-row and hulless barley, were evaluated in stripe rust nurseries at Toluca, Mexico during 2007, Quito, Ecuador during 2007 and 2008, and Pullman and Mt. Vernon, USA [Pacific Northwest (PNW)] during 2007–2009. Disease screening data for barley stripe rust resistance at multiple locations and seasons were analyzed using factor analysis (FA), principal component analysis (PCA) and analysis of variance (ANOVA). Factor analysis led to the removal of a number of disease assessment variables that had inadequate disease levels or an unsuitable rating scale. The PCA scores revealed that the two-row types of barley were generally more resistant than the six-row and hulless types. ANOVA indicated that the effect of seasonal influence on screening was small, while stripe rust susceptible and resistant barley types were differentiated significantly on mean values averaged on infection type (IT) and percentage diseased leaf area (disease severity, DS) during the 3-yr tests in multiple screening nurseries. The significant correlations in disease assessments between IT and DS suggest that either assessment can be used to replace the other without a significant loss of information regarding barley stripe rust reactions. The first principal component is a useful criterion for assessing stripe rust reactions in barley lines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".