Influence of Different Pretreatments on Germination in Adversity of Atriplex L.Seeds
Bibliographic record
Abstract
Many Atriplex L. seeds are difficult to germinating. To seek for effective methods to improving their germinating ability,we pretreated three Atriplex L. seeds by using distilled water,0.4%,0.8% and 1.2%NaCl solution,10%,15% and 20%PEG solution and low temperature (5℃) and determined germination percentage/ seedling emergence and embryo development under drought stress and salt stress (NaCl),the results showed that: in laboratory germination test,pretreatments above did not enhance the seeds germinating ability significantly in whole,meanwhile,the effects of pretreatments were different depend on the kinds of plants,pretreated ways and germination environments,and represented inconspicuous regularity; in greenhouse test,seedling emergences are significantly enhanced by pretreatments on A.canescens ssp.canescens var.laciniata Parish and A.canescens (Pursh) Nutt.,but is decreased on A. canescens ssp.aptera. In sum,different kinds of pretreatments disturbed germinating ability in adversity a certain extent.
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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) | 0.001 | 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".