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Record W2061165673 · doi:10.5586/aa.2014.006

Comparison of the ability of fifteen onion (Allium cepa L.) cultivars to accumulate nitrates

2014· article· en· W2061165673 on OpenAlexaboutno aff
Renata Wojciechowska, Anna Kołton

Bibliographic record

VenueActa Agrobotanica · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGarlic and Onion Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCultivarDry matterAlliumBulbHorticultureChemistryAgronomyBiology

Abstract

fetched live from OpenAlex

<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>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.143

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.299
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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