Effects of Alkalinity-Induced Iron Deficiency on Physiological and Growth Variables of Some Upland Rice Cultivars under Laboratory Condition
Bibliographic record
Abstract
The prevalence of iron deficiency in upland rice under alkalinity stress is capable of constraining its production. This investigation aimed to explicate the physiological basis of iron deficiency tolerance in some upland rice genotypes. Eighty upland rice genotypes were characterized for iron deficiency tolerance at seedling growth stage in sand-culture hydroponics with varying NaHCO3 concentrations (0, 15 and 25 mM). The treatments were arranged in a completely randomised design with three replicates. A significant decrease was observed on leaf iron concentration, SPAD meter readings, leaf photosynthetic efficiency, quantum yield and growth variables with increasing concentration of NaHCO3. The iron tolerance index was further estimated based on these parameters and used for ranking the genotypes. Based on iron tolerance index, genotypes were divided into six groups, with Caipo and NERICA 7 identified as the most and least tolerant to iron deficiency, respectively. The basis of iron deficiency tolerance is discussed in relation to the stability of the photosynthetic apparatus and the plan growth under alkalinity stress.
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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) | 0.001 | 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".