Effects of Smoke-water on Photosynthetic Characteristics of Isatis indigotica Seedlings
Bibliographic record
Abstract
Smoke-water (SW) had been reported to improve the growth of Isatis indigotica, a Chinese medicinal plant. However, there were very few reports on the mechanism of smoke-water improving plant growth. In this study the effects of smoke-water on the photosynthetic characteristics of I. indigotica seedlings were investigated for the purpose of understanding the mechanism behind this improved plant growth. The results showed that net photosynthetic rate (Pn) was increased by smoke-water, reaching a maximum on 15, 5 and 15 d after treatment with smoke-water at dilutions of 1:500, 1:1000 and 1:2000 respectively. Transpiration rate (Tr) and stomatal conductance (Gs) both showed similar trends to Pn, however, intercellular CO2 concentration (Ci) was decreased with smoke-water treatment. The Fv/Fm was not significantly influenced by smoke-water treatment. The ?PSII was markedly promoted with the application of smoke-water (1:1000) compared with the control and the coefficient of photochemical quenching (qP) showed a similar trend to ?PSII. However the coefficient of non-photochemical quenching of chlorophyll (NPQ) was decreased with treatment of smoke-water. These findings indicate that smoke-water treatment induce an increase in photosynthesis and suggest the main factors leading to this might be the improved stomatal conductance and the enhanced level of the photochemical efficiency of PSII in leaves.
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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 | 0.001 |
| 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".