The Effects of Seepage Water from Tailing Dam on Germination and DNA Damage of Tomato
Bibliographic record
Abstract
In order to elucidate the influence of seepage water from tailing dam on the growth to circumjacent plants,the seeds of tomato( Lycopersicum esculentum) were treated with 0,20%,40%,60%,80% and 100%seepage water. Damage to tomato was determined by the rate of seed germination and comet assay after treatment. The results showed that the rate of seed germination decreased obviously with increasing seepage water concentration. The rate of seed germination was 63% under the treatment of 60% leakage water,which was close to semi lethal concentration of the seed germination. The rate of seed germination was very low under100% leakage water. The single cell gel electrophoresis Cell comet tail lengthened with the increasing seepage water concentration displayed by. Experimental data showed that there was serious effect to tomato seed germination and cell brought by seepage water from tailing dam.
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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 | 0.001 |
| 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".