Identification of Insects in Crotalaria spp. in an Urban Experimental Area at Brasília, Brazil
Bibliographic record
Abstract
Plants of the Crotalaria genus are of great importance, for example: they stand out as green manure. This work aimed to verify the occurrence of insects in C. juncea, C. spectabilis and C. ochroleuca. The experiment was performed out in an urban experimental area at Faculdade ICESP de Brasília, Águas Claras unit, Distrito Federal, Brazil, from March to June 2018. The experiment was installed in a randomized block design with nine experimental units of 1 m2. Three beds were divided into three experimental units for 1 m2, 50 seeds were planting at a spacing of 10 cm between rows and 10 cm between plants. The plants were evaluated weekly, where all insects were collected, counted and stored in glass jars with 70% alcohol. Plants were analyzed for attack and mortality was recorded for 11 weeks. The data were submitted to ANOVA and the Tukey test (α ≤ 0.05) using the R program. As a result, 767 insects from five orders and 10 different families were collected in the three species of Crotalaria used in this study, C. juncea was the species that attracted the most insects (415) and C. spectabilis presented the lowest number (126). Diabrotica speciosa caused great damage to species studied, especially C. juncea, and could become a harmful insect of importance for culture. Additionally, U. ornatrix was found in all three species, but in greater numbers in C. juncea and C. spectabilis.
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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 | 0.001 | 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) | 0.001 | 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".