Influence of Strontium,Cesium,Uranium Upon the Seed Germination of Five Plants
Bibliographic record
Abstract
A germination test of sunflower,soybean,rape,corn and cucumber seeds treated with 0,0.1,0.5,1.0,2.5,5.0,7.5,10.0 mmol/L concentrations of strontium(Sr) nitrate,cesium(Cs) nitrate and uranium(U) nitrate was conducted to study the effects of nuclides on the seed germination of plants and provide the basess for phytoremediation.Results indicate that nuclides and their concentrations influence upon the seed germination rates of plants,and the influences of uranium is more than those of strontium and cesium.The influences of nuclide,concentrations and their interaction upon seed germination rates are very significant.The seed germination rate of corn has a little change to Sr,Cs,U and their concentrations.Those of sunflower,rape and cucumber have a little change to Sr,Cs and their concentrations,but a big change to U and its concentrations.That of soybean has a fluctuation change to Sr,Cs,U and their concentrations.There is very signficant negative correlation(r=-0.928 9**)between nuclide concentration and germination rate of plant seeds in general,but the relevance of differen nuclides and plants are different.The effects of plants,plants×nuclides,plants×concentrations and plants×nuclides×concentrations upon seed germination rates all are very significant.The low nuclide concentration less than 0.5 mmol/L can promote seed germination of plants,and the nuclide concentrations more than 1.0 mmol/L would decrease seed germination rates on the whole.The effects of concentrations of Sr,Cs and U on the germination rate of plant seeds are different.
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 | 0.001 |
| 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".