Structural equation modeling of the Canadian flax (<i>Linum usitatissimum</i> L.) core collection for multiple phenotypic traits
Bibliographic record
Abstract
Zhang, T., Lamb, E. G., Soto-Cerda, B., Duguid, S., Cloutier, S., Rowland, G., Diederichsen, A. and Booker, H. M. 2014. Structural equation modeling of the Canadian flax (Linum usitatissimum L.) core collection for multiple phenotypic traits. Can. J. Plant Sci. 94: 1325–1332. Flax seed yield is a complex trait that results from the inter-relationship between many crop characteristics. Structural equation modeling (SEM) is a statistical method used to determine the relationship between measured variables such as crop characteristics. Crop phenology, canopy traits, yield, and its components were included in structural equation models to determine how these crop characteristics relate to seed yield in a phenotypically diverse collection of flax germplasm. Early season vigor (scored as greater plant stand) was positively associated with canopy light interception and higher seed yield. Plant height also had a significant positive effect on seed yield. Moreover, yield components such as 1000-seed weight, number of bolls per unit area, and boll weight were strongly and positively correlated with seed yield. Focusing on yield-related traits, canopy architecture and expansion, and seed weight may be advantageous over yield per se in breeding for yield improvement.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".