Study of Bacterial Strains from Paddy Soil on Seeds Germination of Lycopersicon esculentum Mill
Bibliographic record
Abstract
The germination of seeds not only depends on the condition of water,light,temperature,oxygen but also affected by the soil microbes,soil salinity,mineral elements.This paper investigated the effects of soil microbes on germination rate,germination tendency and germination index of Lycopersicon esculentum Mill seeds.Lycopersicon esculentum Mill seeds germination experiment was conducted under the treatment of paddy soil and sterilization paddy soil,the results showed that germination rate,germination tendency and germination index of Lycopersicon esculentum Mill seeds were 95%,79% and 32.95 under the paddy soil;germination rate,germination tendency and germination index of Lycopersicon esculentum Mill seeds were 81%,67% and 28.32 under the treatment of sterilization paddy soil.Three microbial strains T,B and H were screened from the soil of rice field,The results showed that effect of strain T on the germination rate,germination tendency and germination index of Lycopersicon esculentum Mill seeds were 90.67%,68.00% and 30.88;Effect of strain B on the germination rate,germination tendency and germination index of Lycopersicon esculentum Mill seeds were 92.00%,65.33% and 31.04;Effect of strain H on the germination rate,germination tendency and germination index of Lycopersicon esculentum Mill seeds were 92.00%,74.00% and 30.68.
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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 | 0.000 |
| 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 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".