Effect of Chitosan Composite on Germination of Pinus tabulaeformis Seeds and It's Film-coating Agent Property Seeds
Bibliographic record
Abstract
Seed coating film-forming agent is an important key functional additives and ingredients,scholars great concern to chitosan which has good film-forming and biological activity.This paper prepared 0.2% and 0.5% chitosan film-forming agent and different rate chitosan composite film,which chitosan were mixed in different proportions to polyvinyl alcohol and nano-TiO2.By film forming property and the coated seeds germination experiments,determined the best film-forming agent on pine seed.The results demonstrated that all the film-forming agents could fast filmed,and chitosan composite film could improve the film character and water resistance,coating loss rate analysis also showed that the coating strength significantly enhanced.Seed germination characteristics analysis showed that among the different ratio of film-forming agent significantly impact the seed germination,germination rate and vigor index.Composite indicators,the best formula was 0.2% CS+0.03-g nano-TiO2,compared with ck,the germination rate,germination energy,germination index and vigor index improved 22%,64%,15% and 59%,and the performance of film-forming properties was also in the best status.
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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) | 0.001 | 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".