Graphic analysis of various sulfur applicationson safflower fatty acids profile
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Bibliographic record
Abstract
In this study, we examined the effects of seven different sulfur treatments on safflower seeds. The treatments included: no sulfur application (S0), 25 kg/ha of pure bulk sulfur (S25), 50 kg/ha of pure bulk sulfur (S50), 25 kg/ha of sulfur phosphate (Sp25), 50 kg/ha of sulfur phosphate (Sp50), 25 kg/ha of zinc sulfate (Zs25), and 50 kg/ha of zinc sulfate (Zs50). Our evaluation covered various seed quality attributes, including ash percentage (ASH), oil percentage (OIL), and protein percentage (PRO). Additionally, we analyzed the fatty acid composition, including palmitic acid 16 : 0 (PAL), stearic acid 18 : 0 (STE), oleic acid 18 : 1 (OLE), linoleic acid 18 : 2 (LINL), arachidic acid 20 : 0 (ARA), and linolenic acid 18 : 3 (LINN). The vector-view of the biplot illustrated positive associations among the fatty acids STE, PAL, and OLE, whereas ASH exhibited negative associations with OIL, LINL, and LINN. The polygon-view graph was divided into four sectors, with the genotype S50 emerging as the top performer for attributes such as OIL, PRO, LINL, ARA, and LINN. Treatment Zs50 occupied the vertex of another sector and displayed the highest values for palmitic acid PAL, STE, and OLE, while treatment S0 was positioned at the vertex of the next sector, characterized by its high ASH content. By utilizing the ideal tester tool of treatment by trait biplot, we identified OIL as the desirable trait that most effectively represented the data. The qualitative properties of safflower oil were notably influenced by sulfur application, with treatment S50 proving to be the most effective in enhancing these properties.
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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 | 0.005 |
| 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 it