Effects of Chelidonium majus and Ascophyllum nodosum Extracts on Growth and Photosynthesis of Soybean
Bibliographic record
Abstract
Herbal extracts used in agriculture are formulated from plants (or other organisms) as alternatives to synthetic chemicals that could have adverse effects on growers, consumers, or the environment. In this study, the effects of two herbal extracts on soybean were assessed: <em>Chelidonium majus</em> (C7: Celext 07) and <em>Ascophyllum nodosum</em> (ST: Stimulagro). A standardized approach for germinating seeds and monitoring early seedling growth for 15 days (SOP-Soybean) was used to evaluate the effects of these extracts. Growth characteristics, chlorophyll content, and photosynthetic rate were measured on the fifteenth day after sowing (DAS). A combination of <em>C. majus</em> and <em>A. nodosum</em> (C7+ST), both 1 g L<sup>−1</sup>, was the most beneficial treatment and significantly increased shoot height (13.2%), dry mass (10.7%), and photosynthetic rate (20.3%). In a separate experiment, foliar application of the same compounds was performed on the tenth and twentieth DAS, with sampling on the thirtieth DAS. Foliar applications with 1 g L<sup>−1</sup> of <em>A. nodosum</em> (ST) significantly enhanced the dry mass (23.5%), and the photosynthetic rate was increased even at 10 days after application (22.5%). Therefore, seedling exposure to <em>C. majus</em> (C7) and <em>A. nodosum</em> (ST) and foliar applications of <em>A. nodosum</em> (ST) stimulated the growth and development of soybean. These natural compounds seem to have the potential to act as growth stimulants for soybean and should be tested for their capacity to improve field growth and yield.
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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 | 0.000 | 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) | 0.000 | 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".