Effect of Soil Temperature in the Emergence of Maize Seeds
Bibliographic record
Abstract
Temperature is one of the most important factors in the germination process, which influences the absorption of nutrients required for growth and development of the plant, germination uniformity, and seed vigor maintenance. Low temperatures can result in deformation of the leaves and the apex of the plant, and high temperatures hamper the development of them. The objective of this work was to evaluate the ideal temperature values for germination in each genetic material used, highlighting the most suitable for each condition. The experiment was conducted at the Central Seed Laboratory at the Department of Agriculture of the Lavras Federal University, where germination percentage, first germination count, seedling emergence and emergence speed were evaluated. The experimental design was completely randomized, with a factorial scheme of 4 × 5, in which it was analyzed four lines (L30, L64, L63, and L91) at five different germination temperatures (15, 20, 25, 30 and 35 ºC). An analysis of variance was done and for the comparison between means, the Scott Knott test was used, at a 5% probability. It can be observed that the lines L63 and L91 were more susceptible to low germination temperatures and lines L30 and L64 do not tolerate high temperatures. Also, the evaluation of temperature-controlled substrate germination proved to be a promising technique to identify tolerant and susceptible lines for germination in both high and low temperatures.
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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 | 0.000 |
| 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".