Effects of long-term simulated microgravity on tomato seedlings
Bibliographic record
Abstract
Xu, D., Guo, S. and Liu, M. 2014. Effects of long-term simulated microgravity on tomato seedlings. Can. J. Plant Sci. 94: 273–280. Whether plants can adapt to a long-term microgravity environment is crucial to their reproduction in bioregenerative life-support systems in space. This research investigated the effects of simulated microgravity on Lycopersivon esculentum Mill. (cv. Dwarf Red-bell). Several indicators, namely germination ratio, percentage of cell membrane damage, malondialdehyde content (MDA), superoxide anion ([Formula: see text]) content, and mininucleolus, were observed 10, 20, 30, and 40 d after planting (DAP). Simulated microgravity [random positioning machine (RPM) treatment] barely had any effect on germination ratio, but it increased MDA, an index indicating membrane lipid peroxidation. Random positioning machine-treated samples had significantly higher [Formula: see text] content until 16 DAP, but these differences ceased after 21 DAP. Simulated microgravity damaged cell membranes, and the damage severity was positively related to the duration of the simulated microgravity treatment. Mininucleoli were more common in RPM-treated root tips than in the 1×g ones. In conclusion, simulated microgravity seriously disturbed tomato seedling growth by damaging cell membrane integrity, causing the accumulation of hazardous substances, and affecting the cell nucleus structure.
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 | 0.000 |
| 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".