Comparison of the carbohydrate content in apples and carrots grown in organic and integrated farming systems
Bibliographic record
Abstract
The aim of this study was to compare some quality parameters of apples and carrots from organic and integrated farming systems. In addition, the cultivars of carrots were grown in two plant densities (600 or 900 thousand plants per hectare). The fructose, glucose, saccharose and dry matter content of seven apple cultivars (Florina, Zvonkové, Topaz, Šampion, Ontario, Melrose and Idared) and two carrot cultivars (Afalon F1 and Cortina F1) were analysed by HPLC and gravimetric methods, respectively. Significant differences were found between organic and integrated apple samples. The interactions between cultivars and farming methods were also significant (p <0.0001). The dry matter and sugar level tendencies were not the same for all apple cultivars. Conversely, more consistent data were obtained for the two carrot cultivars. The bio carrots of both cultivars showed significantly lower dry matter content (p = 0.0004) and higher carbohydrate content (pfructose = 0.0303, pglucose = 0.0003, psucrose = 0.0083) than the samples from integrated production. Other factors like cultivar and plant density also played an important role in sugar content in carrots. Different densities of plants significantly affected the glucose content (p = 0.0373). Cultivar Aftalon F1 showed higher concentration of monosaccharides compared to Cortina F1 (pfructose = 0.0001 and pglucose <0.0001).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".