Effect of Mechanical Damage on to Emergence Rate and Emergence Force of Some Wild Sunflower (Helianthus L. Spp.) Seeds
Bibliographic record
Abstract
To investigate the possible use of wild sunflower species (Helianthus spp.) with cultural varieties at classical breeding studies and to overcome the seed dormancy and germination problems encountered at germination stage in seed, the effect of mechanical damage to the seed on to emergence rate and emergence force was determined. The study was conducted at Uludağ University, Agricultural Faculty, Field Crops Department, Plant Tissue Culture Laboratory and green house in 2014. Seeds of wild sunflower genotypes were obtained from 3 different sources (USDA-USA, Germany, Canada). The seeds imbedded in water one day were scratched carefully from embryo sites and then placed into viol containing 1 : 1 peat: soil mixture (v/v) at 24 ± 2oC in 16h/8 h (light/dark) in the growth chamber. Emergence rate and emergence force values were taken after 10 and 17 days from seeding. Emergence rate and emergence force values varied from 0 - 100% depended on the genotype. The highest emergence rate values (100%) were obtained from genotypes numbered as 4, 17, 20, 24, 37, 59 while genotypes numbered as 31, 32, 33, 38, 39, 40, 41, 46, 47, 48, 49, 51, 52, 55 did not have emergence rate values (0%). The highest emergence force values (100%) were obtained from genotypes numbered as 4, 17, 19, 20, 24, 37, 59 while genotypes numbered as 31, 32, 33, 38, 39, 40, 46, 48, 49, 51, 52, 55 did not have emergence force values (0%)
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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 | 0.000 | 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 | 0.001 |
| 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".