Salinity Influence on <i>Leymus chinensis</i> Characteristics in a Temperate Meadow Ecosystem
Bibliographic record
Abstract
Salinity is an important restrictive factor for plant growth and ecosystem productivity. However, the endogenous mechanisms by which salinity constrains plant growth are not well understood. To determine the mechanism by which soil salinity suppresses plant growth under salt stress, the effect of soil salinity on hormones in the leaves of Leymus chinensis and the plant density, height and biomass were examined in Songnen meadow steppe. The plants with rhizosphere soil were collected in the growing season (May, June, July, September, October) from the field at different salt levels. The shoot density, height and biomass accumulation of L. chinensis highly decreased with the increase in the soil salinity. Salinity significantly reduced the synthesis of the hormones gibberellic acid (GA3) and indoleacetic acid (IAA), but it increased the concentration of abscisic acid (ABA). Significant negative correlations between the soil electrical conductivity and plant leaf hormones (GA3, r = -0.853, P < 0.05; IAA r = -0.971; P<0.01) related to plant growth and positive correlation with ABA (r = 0.931, P<0.01) were observed. Significant positive correlations between the plant hormones related to plant growth (GA3 and IAA) were observed, but negative correlations were found between ABA and plant density (r = -0.872, P<0.05) and height (r = -0.833, P<0.05). The results suggest that soil salinity might restrict plant growth and biomass accumulation by reducing the synthesis of GA3 and IAA and increasing the synthesis of ABA under salt 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".