Influence of Temperature and Water Availability on Seed Germination of Cicer Milkvetch (Astragulus Cicer L.) and Purple Prairie Clover (Dalea Purpurea Vent. Var. Pupurea)
Bibliographic record
Abstract
Global warming may leads a decrease in plant diversity and increased risk for some plant extinction does exist. The effect of temperature and water availability on seed germination were investigated in many plants, and are two of the most important factors on seed germination, for the plant survival that can result in a loss or increase under global climate change scenarios, by affecting a plant's recruitment success. Therefore, research on how climate change affects seed germination is essential for our research and ability to predict the risk for plants. To examine the possible effect of climate change on two commonly grown legumes a greenhouse experiment was run at at AAFC-SPARC. One was an introduced legume, cicer milkvetch, and the other a native legume, purple prairie clover. Our findings were: the experiment with warmer temperature and decreased soil moisture to research seed germination, the result is that reduction in the total both seed germination rate occurs, and showed the seed germination of purple prairie clover is better for the more stressful temperatures and water potentials examined in this experiment. For both legumes were examined on the control water potential the best temperature range is from 20℃ to 30℃ .
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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".