TEXTURE CHARACTERISTICS OF SELECTED CARROT VARIETIES FOR THE PROCESSING INDUSTRY
Bibliographic record
Abstract
The research was conducted on seven industrial carrot varieties: Bangor, Canada, Carlo, Fayette, Kazan, Kathmandu and Maxima and one lineage - Nun 7375. The carrots were grown under identical agritechnical conditions on an Experimental Farm of the Warsaw Agricultural University inelazna near Skierniewice for two consecutive years, 2000 and 2001. The texture of carrot roots was evaluated by means of pene- tration and compression tests. The penetration and compression curves obtained (in force-shift system) were analysed with the INSTRON IX SERIES Automated Material Testing System ver. 8.04. The significance of difference between the mean values of the texture discriminants exam- ined was determined by analysis of variance (Duncan's test). The calculations were done with STATISTICA TM 6.0. All of the carrot varieties exam- ined varied significantly (p<0.05) in the parameters analysed. Only two varieties, i.e. Maxima and Kathmandu, were characterised by the most sta- ble texture in two consecutive years of research. The variety Maxima turned out to be the hardest and firmest, whereas the results of measurements obtained for the roots of the variety Kathmandu were opposite. In most cases, weather conditions and agritechnical treatments in particular years of cultivation had a considerable effect on root texture in the varieties examined. The place and method of sampling were also of great impor- tance in terms of the tests applied. The experimental results indicate that both tests are complementary and should be conducted together.
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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 | 0.002 | 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".