Study on Photosynthetic Characters of Ten Apple Cultivars in Middle Region of Guizhou Province
Bibliographic record
Abstract
To provide the basis for cultivar screening and technical application of apple in middle region of Guizhou Province,the photosynthetic characters of ten apple cultivars were investigated.The results showed that the photosynthetic characters varied among the cultivars.Four groups were obtained by cluster analysis based on photosynthetic rate (Pn),and the Pn of Meng \[12.44 μmol/(m·s)\] was the largest,which was followed by New Gala,Yanfu No.1,Changfu No.2,Fuji 2001 and Benishogun.The light compensation point (LCP) diversified from 15 (Changfu No.2) to 70 (Honggailu) μmol/(m·s),and the light saturation point (LSP) ranged from 1080 (New Gala) to 1330μmol/(m·s)(Yanfu No.1).The response equations of Pn to photosynthetic active radiation (PAR) were parabolic,with the exception of New Gala.Also,the response equations were obtained between Pn and temperature (T),and the optimal T varied from 26℃ to 30℃,which different from the cultivars.Pn was closely correlated to stomatal conductance (Sc),conversely,the correlation between Pn and intercellular CO2 concentration (CO2int) was negatively significant.No obvious correlation was obtained between Pn and relative humidity (Hr).Collectively,PAR was the most important environmental factors for photosynthetic in the locality,ie.the importance of other factors were lesser relatively.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".