Chlorophyll content, leaf gas exchange and growth of oriental lily as affected by shading
Bibliographic record
Abstract
We evaluated the effects of light conditions on leaf gas exchange, chlorophyll content, and growth responses in the Oriental lily (Lilium auratum L.) cv. Sorbonne. The experiment involved application on increasing shade densities (0, 60, 75, and 80%) to Sorbonne, and was carried out in the Horqin Sandy Land of northern China. Shade tests showed that growth of the lily was primarily affected by the level of irradiance. Photoinhibition occurred in the 0 and 60% shade treatments, but not in the 75 and 80% shade treatments. Shade treatments led to increase in photosynthetic pigment content, enhancement in photosynthetic efficiency, and finally increase in the commercial value of the lily. P n, on the other hand, was lowest in the 80% shade treatment. So irradiance less than that achieved in the 80% shade treatment limited carbon assimilation and led to decreased plant growth. Plants grown under 75% shade displayed the optimal traits determining commercial value (plant height, flower length, flower diameter). Trends in P max, AQY, LSP and LCP to shade confirmed that the lily is a shade-tolerant plant. Excessive light, therefore, was the primary factors limiting lily quality. Growth under conditions of 75% shade is recommended to improve photosynthetic efficiency and alleviate photodamage, thus increasing the commercial value of the lily grown in the Horqin Sandy Land.
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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) | 0.000 | 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 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".