Leaf Chlorophyll Content and Agronomic Performance of Bt and Non-Bt Soybean
Bibliographic record
Abstract
The objective of this study was to evaluate the levels of chlorophyll and agronomic performance of Bt and non-Bt soybeans. For the evaluation we used the chlorophyll meter (SPAD-502), which was collected randomly in the upper third (TS), middle third (TM) and lower thirds (TI). Evaluations were performed at 7, 14, 21, 28, 35, 42, 49, 56, 63, and 70 days after emergence (DAE). Used the randomized experimental design in a split plot design (2 x 3 x 10) with four replicates for analysis of chlorophyll content and factorial 2 x 2 (two cultivars and two regions) with four replications for the factors of production. For the region of Dourados, the highest chlorophyll levels were presented to 42 DAE for soybeans Bt and non-Bt, the TS and TI, for TM at 42 DAE for soybeans Bt and non-Bt to 35 DAE. In Douradina the highest levels of chlorophyll were for soybean at 28 DAE Bt and non Bt at 49 DAE in the lower third. For TM and TS cultivars Bt and non-Bt had higher chlorophyll content at 35 DAE. From the results obtained, it can be concluded that the Bt technology did not influence the chlorophyll content of soybean, the two cultivars showed similar levels, with higher concentrations in the middle third of the plants in the two regions studied. For agronomic attributes, plant height, first pod height, number of pods per plant and yield, Bt soybeans had higher values compared to non-Bt soybeans in two environmental studies.
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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".