Evaluation of Drought Resistant of Different Processing Tomato at Seed Germination Stage under PEG-6000 Stress
Bibliographic record
Abstract
To study the drought resistance of the seeds of 3 processing tomato under different PEG-6000 concentrations osmotic stress.The experiment set six PEG-6000 concentrations osmotic stress,which are respectively 0、5% 、7.5% 、10.0% 、12.5% 、15.0%;The seeds of processing tomato were treated under drought resistance and the seeds germination rate,germination potential,germination index,vitality index were measured.It was shown that(1) with the increasing of PEG concentrations,the germination rate,germination potential,germination index,vitality index,root fresh weight and dry weight,hypocotyl fresh weight and dry weight all decreased gradually.The change tendencies of different drought resistance among difference provenance were similar,but the range of changes was different remarkably;(2) under different osmotic potentials(PEG-6000) to evaluate their drought resistance using the fussy subordinate function,the results indicated that the drought resistance of the 3 processing tomato decreased in the order as: KT-16,KT-18,KT-70;(3) the drought resistance index could better reflect the differences among different processing tomato under PEG concentrations osmotic stress and could be used as a filtrating method appraising drought resistance in processing tomato germination stage.
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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".