Modeling Genetic Effects on the Photothermal Response of Soybean Phenological Development
Bibliographic record
Abstract
The identification of genes that affect plant growth and development has played a prominent role in modern plant research. Mathematical modeling can be a useful tool in this process of quantifying the effects of individual genes. In soybean [Glycine max (L.) Merr.], seven loci (E1 to E7) have been identified that condition time to flowering and maturity and photoperiod sensitivity. Twenty‐nine near‐isogenic lines with different combinations of alleles at six of these loci in either ‘Clark’ or ‘Harosoy’ background were used in this study. Days from planting to first flower were observed in these lines over 2 yr at two locations (Ottawa, ON, Canada, and Urbana, IL, USA) under natural daylength and a 20‐h photoperiod. A mathematical model was developed to simulate the effect of average daily temperature, photoperiod, and the temperature × photoperiod interaction. A photoperiod coefficient was calculated for each isoline, which resulted in an R2 of 0.93 when calculations of times to first flower were correlated with observations. A submodel was developed to calculate photoperiod coefficients by adding contributions from each locus with dominant alleles. This reduced the 29 isoline coefficients to seven coefficients (one for each locus plus an additional value for unknown genes) but with a reduction of the R2 of from 0.93 to 0.89. The E1 coefficient was approximately twice the size of the other five allele coefficients. Time from planting to first flower can be calculated from the average daily temperatures and latitude of a given location using the gene model if the genetic makeup of the line is known.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".