Al 3+ - Ca2+ INTERACTION IN PLANTS GROWING IN ACID SOILS: AL-PHYTOTOXICITY RESPONSE TO CALCAREOUS AMENDMENTS
Bibliographic record
Abstract
High aluminum (Al) concentrations as Al3+ represent an important growth and yield limiting factor for crops in acid soils (pH ≤5.5). The most recognized effect of Al-toxicity in plants is observed in roots. However, damages in the upper parts (including stem, leaves and fruits) may also be present. In addition, Al-toxicity triggers an increase in reactive oxygen species (ROS), causing oxidative stress that can damage the roots and chloroplasts, decreasing normal functioning of photo synthetic parameters. Al-toxicity may also increase or inhibit antioxidant activities, which are responsible to scavenge ROS. As result of the negative effects of toxic Al, root metabolic processes, such as water and nutrient absorption, are disturbed with a concomitant decrease in calcium (Ca) uptake. Ca plays a fundamental role in the amelioration of pH and Al-toxicity through Al-Ca interactions improving physiological and biochemical processes in plants. Ca is a useful amendment for correcting these negative effects on crops growing in acid soils. This is an agronomic practice with alternatives, such as limestone or gypsum. There is little information about the interaction between amendments and Al-toxicity in physiological and biochemical processes in crops. Thus, the main objective of this review is to understand the interactions between Al3+ and Ca amendments and their effects on the physiology and biochemical responses in crops growing in acid soils.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".