Suitability of Different Gels as Seed Carriers and Germination and Emergence Promoters in Processing Carrots
Bibliographic record
Abstract
Stand establishment is critical for optimizing yield and quality in carrots. Low soil temperatures and moisture conditions often challenge seed germination and emergence. Providing an artificial exosperm with appropriate germination promoters, stress conditioners and growth invigorators to the seed may facilitate uniform germination and emergence. Germination patterns and velocity of germination of `Oranza' carrot was studied. Seeds were mixed in different types of gels at various ratios and incubated at either 5 or 20 °C. Gels used were Laponite RD, Laponite RDS, guar gum, algenic acid, and agar. Data on germination percentage was collected and the velocity of germination was calculated. Germination was delayed at 5 °C. Both at 5 and 20 °C, Laponite RD promoted and enhanced germination and vigor, and resulted in the highest mean germination percentage (90% at 20 °C and 89% at 5 °C). Laponite RD at 2.5% has shown an overall advantage in germination percentage (94% at 20 °C and 87% at 5 °C) over other gel types and concentrations.
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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".