Cellular mechanisms and strategies for salinity tolerance (NaCl) in plants
Bibliographic record
Abstract
The problem of salinity is multiple. In addition to salt stress, ion toxicity (Na⁺ and Cl– dissolved in irrigation water or in soil solution), and mineral nutrition perturbation, plants have difficulty absorbing water from soil because of its elevated osmotic pressure, which leads to water stress and thus complicates and impairs their physiological state in an exponential way. Consequently, cells try to adjust their water potential by ion homeostasis regulation via vacuolar compartmentation and (or) extrusion out of the cell of the toxic ions (Na⁺ and Cl–). Nevertheless, if this is not sufficient, the plant has to use another way to face salt stress, which consists in the synthesis and accumulation of a class of osmoprotective compounds known as compatible solutes, mainly amino compounds and sugars. Energetically, this osmotic strategy is more expensive than ion homeostasis regulation. A secondary aspect of salinity stress in plants is the stress-induced production of reactive oxygen species leading to an oxidative stress whose damage reduction could be realized via the production of antioxidants. Perception and signal mechanisms represent the first events of plant stress adaptation, and the main pathways followed are calcium, abscissic acid (ABA), mitogen-activated protein kinases (MAPKinases), salt overly sensitive (SOS) proteins, and ethylene.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".