Genotype–Environment Analysis of Parameters Describing Water Uptake in Natto Soybean
Bibliographic record
Abstract
The amount of water absorbed following a defined soaking period has been studied in natto soybean [ Glycine max (L.) Merr.], but there are no reports of studies of the rate or rapidity of water uptake. This study was conducted to examine the suitability of several water uptake parameters, which define the speed and amount of water absorbed, for analysis of variance and to determine the extent of genotype, environment, and genotype × environment interaction effects on water uptake parameters. Two larger‐seeded checks and 14 natto lines were grown at six locations in 1996 and 1997. Water uptake observations were taken at 0, 1, 3, 6, and 24.5 h using seed from individual plots. The exponential rise to a maximum equation, Y = a [1 − exp(− bX )], was fitted for each plot. The parameters a at 16 h (a16) and b , best fit the assumptions for analysis of variance. Genotype, year × location, genotype × year, and genotype × location × year effects were significant for these parameters. Location effects were significant for a16 but not for b Year and genotype × location effects were not significant. In this study, genotype × environment effects were relatively unimportant, while the main effect of genotype was more important. The b parameter provides additional information compared with the use of a16 alone. Heritability estimates for water uptake parameters (a16, H = 0.36 ± 0.16; b , H = 0.42 ± 0.46) were low to moderate on a plot basis. It seems possible to select for both rapid and high final water uptake in natto soybean.
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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) | 0.000 | 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 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".