Efeito de fertilizantes e tipos de plantio no perfil de ácidos graxos de cártamo
Bibliographic record
Abstract
Soils in semi-dry areas lack sufficient nutrients for crops, so their yield depends on fertilizers and planting methods. This study aimed to investigate how the fatty acids in safflower oil change with the use of fertilizers and planting methods. Five fertilizers [F1: control; F2: 10 t ha-1 of farmyard manure; F3: 20 t ha-1 of farmyard manure; F4: NPK (130:60:45 kg ha-1); F5: nano-Fe and nano-Zn] and four planting patterns (P1: 40-cm furrow planting; P2: 60-cm furrow planting; P3: 40-cm ridge planting; P4: 60-cm ridge planting) were tested. A biplot analysis explained 73 % of the variability, with the first and second principal components accounting for 54 and 19 %, respectively. The polygon-view was divided into five sections, with F3-P4 being the best for oil and protein contents, stearic (18:0), oleic (18:1), linoleic (18:2), linolenic (18:3) and lauric (12:0) acids, and iodine value. The vector-view showed a positive association among iodine value, oil content and linoleic, oleic, linolenic, stearic and lauric acids, as well as between myristic acid (14:0) and protein content, and between saponification and peroxide values. The best treatments were F4-P4, F4-P3 and F5-P4. Therefore, using NPK chemical fertilizer or nano-Fe and nano-Zn, combined with planting on a ridge with a 40 or 60-cm interrow distance, can be beneficial for producing high-quality safflower oil. KEYWORDS: Carthamus tinctorius L., farmyard manure, biplot analysis.
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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 | 0.001 |
| 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".