Greenbelly Stinkbug Biology in Different Temperatures
Bibliographic record
Abstract
The green-belly stinkbug (Dichelops melacanthus) stands out due to its damage potential towards corn and wheat crops. The population distribution and size greatly influence the potential damage. The insect’s reproductive capacity depends on different temperature conditions during various crop seasons and its lifecycle. The study aimed to evaluate the influence of different constant temperatures on the D. melacanthus biology. The study was performed twice, once in the year of 2017, and again in 2019. The biological features were observed in temperature-controlled climate chambers with the following temperatures: 11, 16, 21, 26, 31 and 36 °C, each with a fluctuation of ±1 °C, using a RH of 65±15% and photophase of 14 h. The evaluations were conducted from eggs to adults in terms of: nymphal hatch period, each instar duration, female fecundity and egg viability. There were no eggs hatching at 11 ºC and it also reached 100% mortality during the second instar at 16 °C. The eggs-to-adult duration for the temperatures 21, 26, 31 and 36 °C in the trial of 2017 was 58.4, 30.1, 18.2 and 16.3 days, respectively. In the same temperatures, but during the 2019 trial, the eggs to adult duration was 58.1; 29.7; 21.3 and 19.1 days, respectively. The reproductive capacity in the temperatures of 21 and 36 °C impaired the female fecundity and egg viability. The temperatures 26 and 31 °C favored the development of D. melacanthus.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".