Залежність інтенсивності наливу зерна та вологовіддачі від цінних господарських ознак кукурудзи
Bibliographic record
Abstract
Purpose. To analyze the dependence of the corn grain filling intensity and moisture-yielding ability of corn grain on morphological and economic characteristics in each ripeness group of corn lines. Material and Methods. The study was carried out in the Laboratory of Introduction and Preservation of Plant Genetic Resources and the Laboratory of Corn Breeding and Seed Production of the Yuriev Plant Production Institute of NAAS in 2017-2019. The accessions were sown by the standard method in two-row plots of 9.8 m2 in three replications. The reference accessions were placed after every 20 plots: early ripening - F2 line; medium-early – UKh 52; medium-ripening - DS 103, UKhS 126, and SO 125; medium-late - А 619, Kharkivska 215, and KhА 408. During the growing period, the accessions were evaluated in the field 24 times for typicality. In the laboratory, the grain moisture was determined thermogravimetrically four times for each line during its ripening period, every ten days, starting on day 30 after pollination. For comparison, the moisture content in grain was also determined in the field using an AVD 6100 needle moisture meter for wood. Results and Discussion. Lines with the maximum yield of moisture per day were selected: six medium-early lines (LPL 79 A, UKhK 5, UChS 85, UKhK 590, UChS 85, SL 73-85-2 (Ukraine), СО 190 (Canada), and B 267 (Russia)), 24 medium-ripening lines (of them, 20 lines were bred in Ukraine; one line is from Russia (B 321), and three lines are from the USA (W 83, A 619, and B 143)), and 25 medium-late lines (20 Ukrainian lines (UKhK 472, KhLH 78, LNAU 18, ОV 1248, UKh 804, and others), two Russian lines, 1 Kazakhstanian line, and 1 line from the USA). We noted the dependences of the grain filling intensity, moisture release during ripening, and grain drying rate on morphobiological and so-called "specific" characteristics (peduncle length, sheath number, density of sheath adhesion to the ear, grain consistency). Corn lines with intensive grain filling and a set of valuable economic features were distinguished. Having studied inbred corn lines, we found a strong positive correlation between dry matter accumulation and performance (r = 0.90) and moderate positive correlations between dry matter accumulation and kernel number per ear (r = 0.45), between dry matter accumulation and plant height (r = 0.40), and between dry matter accumulation and ear attachment height (r = 0.39). Conclusions. The best lines were selected in each group of ripeness according to the intensity of moisture egress from grain and valuable economic characteristics. In the medium-early group, eight best lines were selected. However, only 6 lines had good economic characteristics. Most of the medium-early lines were superior to the reference accession, UKh 52, in terms of performance and thousand kernel weight. In the medium-ripening group, 24 best lines were selected in comparison with UChS 126 (reference accession).
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 teacher head, 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".