Effects of Different Light Sources on the Growth of Non-heading Chinese Cabbage (Brassica campestris L.)
Bibliographic record
Abstract
To date, little is known about the effects of different light sources on the growth and quality of non-heading Chinese cabbage (Brassica campestris L.). The objective of present study was to evaluate the effects of light-emitting diodes (LEDs) light sources (blue, blue plus red, red), fluorescent lamps and sunlight on growth and vitamin C, soluble protein, sucrose, soluble sugar, starch and pigment concentrations in non-heading Chinese cabbage seedlings. The dry mass of shoots and the fresh and dry masses of roots were highest in seedlings grown under red LEDs with weak lights. The fresh mass of roots and starch concentration were highest under red LEDs despite of the altered photosynthetic photo flux density (PPFD) levels. The concentrations of chlorophylls and vitamin C were greatest under blue LEDs with altered PPFD levels. The numbers of flower buds and open flowers were highest under red LEDs and blue plus red LEDs and were higher under LEDs than fluorescent lamps. The duration of flowering was highest under red LEDs and blue plus red LEDs. The present results demonstrate that LED light sources are more effective than fluorescent lamps for vegetative and reproductive growth of non-heading Chinese cabbage. Moreover, blue LEDs benefit vegetative growth, while red LEDs and blue plus red LEDs support reproductive growth in non-heading Chinese cabbage. In the artificial cultivation and subsequent transplanting of the life cycle of plants, the light source can be selected to meet the requirements of different growth stages of plants and be used to promote the subsequent process in the industrial production of non-heading Chinese cabbage.
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".