Competitive Capacity of Native Species From the Carajás National Forest, Brazil
Bibliographic record
Abstract
The present research had the objective to use the factor analysis in the evaluation of the competitive capacity of three native species from the Carajás National Forest in competition with different plant densities of the Urochloa brizantha grass. The experiment was conducted in a greenhouse and consisted in planting pots with the native species Bauhinia longipedicellata, Mimosa acutistipula and Solanum crinitum in competition with the exotic grass Urochloa brizantha. The exotic grass was established at densities ranging from 1 to 5 plants per pot, composing a 3 × 5 factorial arrangement with four replications that were delineated completely at random. Data were submitted to factor analysis for further analysis of variance and Tukey’s test at a 0.05 level of significance with the extracted factors. The effects of U. brizantha densities were evaluated by regression analysis. Out of the four extracted factors, three could be interpreted and were defined as vegetative growth index, infestation density index and physiological quality index. The Solanum crinitum species was slightly greater than the others in terms of vegetative growth rate and physiological quality. Generally speaking, native species maintain their vegetative growth in competitive conditions with up to two Urochloa brizantha plants; above that, the vegetative growth index tends to zero, while the infestation density index becomes positive.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".