Effects of waterlogging on the yield and growth of summer maize under field conditions
Bibliographic record
Abstract
Ren, B., Zhang, J., Li, X., Fan, X., Dong, S., Liu, P. and Zhao, B. 2014. Effects of waterlogging on the yield and growth of summer maize under field conditions. Can. J. Plant Sci. 94: 23–31. A field experiment was performed to study the effects of waterlogging for different durations (3 and 6 d) on the yield and growth of summer maize at the three-leaf stage (V3), six-leaf stage (V6), and the 10th day after the tasseling stage (10VT). The results after 2 yr indicated that maize development and grain yield responses to waterlogging depended on both stress severity (intensity and duration) and different growth stage. Yield decreased significantly with an increased waterlogging duration during V3 and V6. The yields of maize hybrid Denghai 605 (DH605) in treatments V3-3, V3-6, V6-3, V6-6, 10VT-3, and 10VT-6 were 23, 32, 20, 24, 8, and 18% lower than those of the control (CK), respectively; Yields of Zhengdan 958 (ZD958) were lower by 21, 35, 15, 33, 7, and 12%, respectively. Waterlogging also affected the growth and development of summer maize. Ear characteristics (grains per ear and 1000-grain weight) and plant morphology (plant height, ear height, and leaf area index) decreased, whereas the bald tip length increased significantly. The maximum grain-filling rate decreased under waterlogging; furthermore, the dry matter accumulation decreased and dry matter distribution proportions of the stem and leaf increased. However, the distribution proportion of grain decreased. Maize was most susceptible to waterlogging damage at V3, followed by V6 and 10VT, with damage increasing with increasing waterlogging duration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".