Comparison of the ability of fifteen onion (Allium cepa L.) cultivars to accumulate nitrates
Bibliographic record
Abstract
<p>The aim of a two-year study was to characterise selected <em>Allium cepa </em>L. genotypes with regard to their ability to accumulate nitrates in bulbs as well as to search for a possible relation between NO<sub>3</sub><sup>-</sup> concentration and dry matter content. Fifteen cultivars of edible onion, mostly of long-day genotype with different growing periods, bulb size and skin colour, were taken for the experiment. Seeds of particular cultivars were obtained from the following seed companies: Spójnia Nochowo (‘Labrador’, ‘Takstar F<sub>1</sub>’,‘Tęcza’, ‘Warna’, ‘Zorza’), Polan (‘Polanowska’, ‘Topolska’), PlantiCo Gołębiew (‘Alibaba’, ‘Efekt’, ‘Kristine’, ‘Niagara F<sub>1</sub>’),and PlantiCo Zielonki (‘Bila’, ‘Irka’, ‘Wenta’, ‘Zeta’). Plants produced from seedlings were grown in the experimental field of the University of Agriculture in Kraków. After crop harvesting and additional drying, nitrate and dry matter content in bulbs of all cultivars were measured.</p><p>The following cultivars: ‘Efekt’, ‘Labrador’ and red-skinned ‘Wenta’, were characterized by the lowest ability to accumulate NO<sub>3</sub><sup>-</sup> in bulbs. The highest nitrate content was noted in bulbs of ‘Takstar F<sub>1</sub>’ (a very early-season cultivar), followed by ‘Bila’ and ‘Tęcza’. A weak, yet statistically significant negative correlation between nitrate and dry matter content was observed. The highest dry matter content was determined in bulbs of white-skinned ‘Alibaba’, while the lowest – in brown-skinned ‘Labrador’.</p>
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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".