Amelioration of chemical and organic fertilizer on photo-inhibition of PSII at photosynthetic noon-break in mulberry leaves grew in saline-sodic soils
Bibliographic record
Abstract
Using the chlorophyll fluorescence kinetics,the effects of application of chemical fertilizer and organic fertilizer on photo-inhibition of PSⅡ at photosynthetic noon-break in mulberry leaves grew in saline-sodic soils were investigated.The results showed that maximal photochemical efficiency of PSⅡ(Fv/Fm)and photosynthetic performance index(PIABS)based on light absorption were only 0.69and0.19,respectively.Meanwhile,photo-inhibition was significant in leaves of mulberry in salt and alkali soil at photosynthetic noon-break.Both chemical fertilizer and organic fertilizer increased the Fv/Fmand PIABS although organic fertilizer were superior than chemical fertilizer.With standardization of OJIP curve,the electronic transmission ability of QAto QBin PSⅡ electronic receptor side increased at photosynthetic noonbreak.The application of chemical fertilizer and organic fertilizer alleviated photo inhibition of PSⅡ photosynthetic noon-break by increasing electron-accepting ability of QBand PQ electronic library,especially,organic fertilizer application also increased electron-accepting ability of QBand decreased QA-accumulation.The application of chemical fertilizer and organic fertilizer increased the activities of oxygen evolution complex(OEC)in PSⅡ electronic receptor side to maintain the normal water cracking function and supply of photosynthetic electron.In addition,the application of chemical fertilizer and organic fertilizer also decreased the dissociation degree of chloroplast thylakoid and increased its adhesion of PSⅡ protein complex to maintain photochemical activity of PSⅡ.The relieve of photo inhibition of PSⅡ photosynthetic noonbreak of organic fertilizer was better than that of chemical fertilizer because the electronic transmission in PSⅡ electronic receptor,while there was few difference between two fertilizers on the condition of OEC and thylakoid membrane.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".