Structural equation modeling in the plant sciences: An example using yield components in oat
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lamb, E. G., Shirtliffe, S. J. and May, W. E. 2011. Structural equation modeling in the plant sciences: An example using yield components in oat. Can. J. Plant Sci. 91: 603–619. Structural equation modeling (SEM) is a powerful statistical approach for the analysis of complex intercorrelated data with a wide range of potential applications in the plant sciences. In this paper we introduce plant scientists to the principles and practice of SEM using as an example an agronomic field trial. We briefly review the history of SEM and path analysis and introduce the statistical concepts underlying SEM. We demonstrate the use of observed and latent variable structural equation models using a multi-site multi-year field trial examining the effects of seed size and seeding density on the plant density and yield of oat in Saskatchewan. Using SEM allowed for insights that a standard univariate analysis would not have revealed. We show that seeding density has strong effects on plant and panicle density, but has very limited effects on final yield. Plant density has a consistent non-linear effect on panicle density across location that was not affected by precipitation. In contrast, the implicit effect of precipitation on seed number appears to be the main driver for final yield. Incorporating precipitation data into the model demonstrates how mechanistic models can be developed by including in the path diagram variables that would normally treated as random factors in a mixed model analysis. Finally, we provide a guideline to assist plant scientists in determining whether SEM is an appropriate method to be used for the analysis of their data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it