Physiological mechanism of tolerance of <i>Lycopersicon</i> spp. exposed to salt stress
Bibliographic record
Abstract
The physiological mechanism of salt tolerance in Lycopersicon spp. was investigated. Ten Lycopersicon spp. were exposed to a gradual NaCl-induced decline in root zone water potential of −0.10 MPa d−1 for 10 d and maintained at −1.065 MPa (221.4 mM NaCl) for a period of 20 d. Growth, water relations and accumulation of ions and compatible solutes, such as proline and the quaternary ammonium compound, trigonelline (methylated nicotinic acid), were studied and correlated. Salt tolerance, measured as growth, in selected Lycopersicon species of varying ecological habitats indicated that L. cheesmanii, native to a saline-coastal habitat, was the most tolerant and L. pennellii, the most sensitive. The commercial cultivar, L. esculentum 'Duke', ranked 7th in the order of relative tolerance to salt. All species accumulated proline in all organs in response to salinity; but there was no general relationship between the ability of these species to accumulate proline and their relative salt tolerance. Relative trigonelline accumulation in meristematic tissues of NaCl-stressed plants correlated with the salt tolerance of these species, however, as did their ability to (1) maintain turgor in the expanding leaves, (2) exclude Na+ from the expanded leaves and (3) exclude Cl− from the root tissues. Key words: Chloride accumulation index, Lycopersicon spp., proline accumulation index, salt tolerance index, sodium accumulation index, trigonelline accumulation index, water potential
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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".