Potassium Phosphite and Water Deficit: Physiological Response of Eucalyptus Using Multivariate Analysis
Bibliographic record
Abstract
This study aimed to evaluate the physiological response of Eucalyptus citriodora Hook seedlings subjected to foliar application of potassium phosphite and water deficit with the aid of multivariate statistical analysis, using MANOVA and canonical discriminant analysis. The experiment was conducted in a completely randomized design with five treatments (0.0 L c.p. ha-1 of potassium phosphite with irrigation; 0.0; 1.25; 2.50 and 5.00 L c.p. ha-1 of potassium phosphite without irrigation) and six replicates, in a greenhouse located in the municipality of Cruz das Almas, Bahia, Brazil. Treatments with potassium phosphite were applied by foliar spray, using the commercial product Reforce from Agrichem® (25.0% K2O + 35.0% P2O5 p/v). Irrigation was suspended seven days after application of the product. The correlation (0.6603) between the evaluated variables indicated that the use of multivariate analysis techniques was adequate to analyze this data set. Eucalyptus plants of the control treatment without irrigation responded to the water deficit conditions with inhibition of photosynthetic activity and increase of free proline content in the leaves. On the other hand, plants which received foliar application of potassium phosphite at highest concentrations (2.50 and 5.00 L c.p. ha-1), even under water deficit conditions, preserved the photosynthetic activity and proline content in the leaves with values equal to those observed in the irrigated control treatment. From this result it is possible to infer about the role of potassium phosphite as an attenuating effect of the water deficit in Eucalyptus citriodora Hook.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".