Spatial Distribution of Nymphs of Triozoida limbata Enderlein, 1918 (Hemiptera: Triozidae) in Guava Orchards
Bibliographic record
Abstract
Triozoida limbata is considered one of the leading pests of guava crop in Brazil. Its nymphs are responsible for sucking leaf borders, causing curling and drying of the leaves, and leaving them with a necrotic appearance. Knowledge of the spatial distribution of nymphs of T. limbata is essential for improving sampling and control techniques. The objective of this study was to perform probabilistic analyses of patterns of spatial distribution of nymphs of T. limbata in guava orchards. The study was conducted in four guava orchards in Ivinhema, Mato Grosso do Sul, Brazil. Samplings were performed every 15 days, from April 2012 to March 2014. To obtain the nymph counts, a sampling area was demarcated in each orchard, comprising 50 sampling units. In each unit, a sample was taken randomly from a shoot of 10 cm to 15 cm in length at the median height of the central plant. Dispersion rates were calculated (variance/mean ratio, Morisita index, and Exponent k of Negative Binomial Distribution) and the data obtained in the field were adjusted to the theoretical frequency distributions (Poisson and Negative Binomial). Following the analyses, we concluded that nymphs of T. limbata in the studied populations were randomly organized in the four areas that were evaluated, and the sampling data have been adjusted to the Poisson distribution model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".