The impact of lime and organic fertilization on the growth of wild-simulated American ginseng
Bibliographic record
Abstract
A 5-yr experiment was undertaken in a red maple forest to evaluate the effects of lime and organic fertilizer application on the growth parameters of wild-simulated American ginseng growing on a very acidi c soil. The application of lime had a positive impact on ginseng emergence and survival rate; it also significantly increased soil Ca, as compared to no application (control). During the last years of the experiment, adding lime also increased leaf area, a s well as shoot and root mass of ginseng. Adding lime and organic fertilizer together positively affected ginseng survival rate and root mass, as compared to adding lime alone. These results indicate that liming can improve the growth and survival of Amer ican ginseng during the first 5 yr of its development in this very acidic maple forest soil. The improvement could be due, at least partly, to increased Ca content in the soil. Addition of organic fertilizer would be beneficial as long as sufficient Ca2+is provided to alleviate the Al toxicity of this soil. Thus, using such cultural practices, wild-cultivation of American ginseng, even in red maple forests, could constitute a valuable alternative to field-cultivation. Key words:
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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".