Effect of Salinity and Potassium on Phytomass and Quality of Guava Rootstocks
Bibliographic record
Abstract
Potassium fertilization is one of the main techniques that has been studied to mitigateeffects of salt stress in plants, probably because potassium reduces the toxic effect of sodium by competitive inhibition and provides greater tolerance to genotypes of plants to salinity. Hence, this study aimed to evaluate the effect of different salinities of irrigation water in the formation of phytomass and quality of rootstocks of guava cv. Paluma, fertilized with increasing doses of potassium, in an experiment conducted using eutrophic Fluvic Neosol with sandy loam texture under greenhouse conditions, in the municipality of Pombal-PB, Brazil. The experimental design was randomized blocks in 5 × 4 factorial scheme, and the treatments resulted from the combination of five levels of irrigation water electrical conductivity (ECw = 0.3; 1.1; 1.9; 2.7 and 3.5 dS m-1) and four K doses (70, 100, 130 and 160% of K), in which the dose of 100% K corresponded to 726 mg of K dm-3 of substrate, with four replicates and two plants evaluated in each plot. Irrigation with water salinity from 0.3 dS m-1 compromises the total dry matter accumulation and the Dickson quality index of guava rootstocks cv. Paluma at 225 days after emergence (DAE), independent of potassium fertilization. Fertilization with different potassium doses did not promote differences in phytomasses and quality of rootstocks. There was no significant effect of interaction (salt × doses of K) on the studied variables.
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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 | 0.001 |
| 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".