Bibliographic record
Abstract
The rhizosphere of ginseng contains a wide range of microorganisms that can have beneficial or harmful effects on the plant. Root exudates of ginseng, particularly ginsenosides and phenolic acids, appear to select for particular microbial populations through their stimulatory and inhibitory activities, which may account for the similarities between the rhizosphere microbiomes of different cultivated species of Panax. Many practices of cultivation attempt to mimic the natural conditions of ginseng as an understory plant in hilly forested areas. However, these practices are often disruptive to soil, and thus the soil microbiome differs between wild and cultivated ginseng. Changes in the microbiome during cultivation can be harmful as they have been associated with negative changes of the soil physiochemistry as well as the promotion of plant diseases. However, isolation of a number of beneficial microbes from the ginseng rhizosphere indicates that many have the potential to improve ginseng production. The application of high-throughput sequencing to study the rhizosphere microbiome of ginseng grown under a variety of conditions continues to greatly expand our knowledge of the diversity and abundance of those organisms as well as their impacts of cultivation. While there is much more to be learnt, many aspects of the ginseng rhizosphere microbiome have already been revealed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".