Germinative and physiological performance of eucalyptus species under abiotic stress
Bibliographic record
Abstract
With the increase in cultivated areas, one of the great challenges faced by eucalyptus growers is abiotic stresses, especially because new areas are often affected by water deficit. Therefore, the identification of tolerant genotypes is important to overcome this problem. Thus, the objective of this work was to characterize the germinative and physiological performance of eucalyptus species under abiotic stress. Four Eucalyptus species ( E. brassiana, E. camaldulensis, E. saligna, and E. urophylla ) were evaluated in two experiments. In the first experiment, the germination potential was assessed under two temperatures (25 and 35 °C) and 11 water potentials of the substrate: 0 (deionized water), -0.05, -0.10, -0.15, -0.20, -0.25, -0.30, -0.40, -0.60, -0.80, and -1.0 MPa. In the second experiment, the species were evaluated in a protected environment under two water regimes comprised by 60% and 20% of the maximum water retention capacity by the soil of the pots. Regarding germination, E. saligna was the most sensitive species to stress, showing only 48% germination on 0 MPa potential (35 °C) and under substrate water deficit it reduced 50% germination on -0.21 MPa (25 °C) and -0.16 MPa potential (35 °C). On the other hand, E. brassiana maintained germination at both temperatures and reduced half of its germination at potentials of -0.33 MPa (25 °C) and -0.32 MPa (35 °C). In protected environment, the physiological metabolism of plants decreased under lower water availability. E. saligna was the species with the highest reduction for the rate of net assimilation and efficiency in water use. In addition, E. camaldulensis showed better physiological performance under water deficit. Thus, E. brassiana and E. camaldulensis would be the most suitable species to be used for breeding program aiming to improve water deficit tolerance. E. saligna is indicated for places with a mild climate and with good water availability. The behavior of the species between the experiments showed that under certain conditions there are correspondences in performance, which indicates that this type of experiment assists in the characterization of species, especially when considering the establishment and initial plant development.
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".