Trinexapac-Ethyl Causes Stimulatory Effect on the Initial Growth of Eucalyptus urograndis Clones
Bibliographic record
Abstract
Eucalyptus plants are sensitive to stress factors in the initial growth phase. The plant growth regulator trinexapac-ethyl can provide gains to eucalyptus plants, a phenomenon known as hormesis. Thus, the aim of this work was to evaluate the effect of trinexapac-ethyl on the initial growth of two Eucalyptus urograndis clones (Clone 1 = I-144 and Clone 2 = 1407). The experiment was performed during 90 days after planting (DAP) of eucalyptus, in 100 L pots, with a randomized complete block design. The treatments were arranged in a 2 × 3 factorial scheme, with two eucalyptus clones and three trinexapac-ethyl doses (0, 30 and 60 g i.a. ha-1), with four replications. Fortnightly, plants height and stem diameter were evaluated. At 90 DAP, the leaf area and plants dry matter (DM) were evaluated. Clone 1 obtained leaf area and total DM 53% and 58% larger than Clone 2, respectively. The higher dose of trinexapac-ethyl provided mean gains of 30% in stem DM and total DM in Clone 1. For Clone 2, both doses of the product caused mean gains of 70% in stem DM, leaf DM and total DM. In conclusion, trinexapac-ethyl has a positive effect on the initial growth of E. urograndis, Clones I-144 and 1407. Clone I-144 showed higher growth than Clone 1407, regardless trinexapac-ethyl application. However, the Clone 1407 obtained more expressive gains with the application of trinexapac-ethyl, compared to Clone I-144.
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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".