Influence of Additive on Growth and Differentiation of Cordyceps militaris (L.) fruitbody
Bibliographic record
Abstract
Objective:Study the growth and differentiation of fruitbody of cordyceps militaris (L.) Link with treatment additive Method:In this experiment,the micro additives were added in the culture medium to screen optimized additive for the high yield,well quality militaris.The compounds were a growth regulator 2,4-chlorophen-oxyacetic acid (2,4-D),corn meal,citric acid triamine,colchicine,and streptomycin.The effect of those on the growth and development of cordyceps militaris was tested in the experiment.Result:The results showed that the growth of sino-canadian regulator (2,4-D) (0.14g/L) and citric acid triamine (0.6g/L) of the medium can have mycelial growth faster,fruitbody differentiation higher,fruitbody growth speeder,fruitbody color deeper and cordycepin concentration higher (0.46%).Corn flour takes mycelial growth faster but later and slower in the cultivationr.Colchicine and streptomycin can induce primordium differentiation,and enhance the capacity of the medium to inhibit other bacterium,increase the yield of fruiting bodies and its cordycepin.Conclusion:the growth regulator (2,4-D),citric acid triamine,colchicine,streptomycin were added to nutrient solution in the cordyceps cultivation to improve the yield of cordyceps fruiting body and the cordycepin contentation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".