Data assessing genotypic variations in selected traditional rice landraces of Jeypore tract of Odisha, India based on photosynthetic traits
Bibliographic record
Abstract
Variations in photosynthetic characteristics and dry matter accumulation were investigated in thirty selected rice (Oryza sativa L.) landraces from Jeypore tract of Odisha, India to find the possibility of their use in crop improvement programs. Leaf gas exchange measurements, photosystem (PS) II activity and leaf pigment estimates were conducted at the flowering stage. Significant differences were noticed in the CO2 photosynthetic rate (PN), stomatal conductance (gs), transpiration rate (E), internal CO2 concentration (Ci), water use efficiency (WUE) and carboxylation efficiency (CE) among the landraces. In addition, significant variation was observed in leaf chlorophyll content, PS II activity and dry matter accumulation (DMA). Further, multiple correlations between photosynthetic characteristics and other physiological traits revealed that leaf photosynthesis was not significantly influenced by PS II photochemical activity, leaf area and pigment contents but it was regulated by stomatal conductance, water use efficiency and carboxylation efficiency. Taken together, data presented here shows that some of the landraces had superior photosynthetic traits along with better DMA under prevailing environmental condition and can be used for future crop improvement programs aimed for an increase of leaf photosynthesis in rice.
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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 | 0.000 | 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".