Effects of Varities Genotype and Temperature on the Agronomic Character of Virus-free Shoot of Potato in Vitro
Bibliographic record
Abstract
Different genotypes and different temperature on shoot fresh weight,shoot dry weight,root fresh weight,root dry weight,the number of leaves,plant height,stem diameter,the length of root and root number were investigated using potato virus-free seedling in Atlantic,Kexin No.1 and Qingshu168 as trial materia,using the method of Randomized block design of two factors.And measured some properties index after training 14days,The results showed that genotypes and temperature had significant interact effection on the root fresh weight,root dry weight and plant height(p0.05),but other properties index had no significant effect.Genotypes could affect shoot dry weight,root dry weight,the number of leaves,and root number(p0.05).Different temperature had significant effect on shoot fresh weight,shoot dry weight,root fresh weight,root dry weight,stem diameter,the length of root and root number(p0.05),but other properties index had no significant effect.So the optimal temperature for the three genotypes was(23±2)℃.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".