Does Deep-Underground Storage Stimulate the Germination of Canola (Brassica napus L.) Seed?
Bibliographic record
Abstract
Agriculture is a crucial area to be considered when exploring and exploiting the use of deep-underground space. We investigated the feasibility of deep-underground seed storage by keeping canola seed in either envelopes or sealed packages at four depths below the Earth’s surface (0, 240, 690, and 1410 m) at a gold mine in northeastern China. We studied the effects of storage depth and duration by conducting germination tests with the stored seed. The results showed that the rate of germination was reduced in seed stored at deeper levels and was also lower at all depths after a more prolonged period of storage. Seeds from sealed packages exhibited better resistance to the deep-underground environment than seeds kept in envelopes. However, measurements of hypocotyl lengths and biomass accumulation revealed that the germination of seeds stored in deep-underground was initially inhibited but recovered well compared with the control as the storage depth increased. The total biomass of the hypocotyl increased as the depth of seed storage deepened, indicating the existence of a compensatory effect on seed germination. The findings suggest that short-term deep-underground storage of seeds in sealed packages would improve the germination performance of cultivated canola in terms of the hypocotyl length and biomass accumulation and might be considered as a pre-sowing strategy.
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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".