Abundance and Dynamics of the Main Heteroptera Pests of Cocoa Tree in the Orchards of the Department of Méagui (South-West, Côte d’Ivoire)
Bibliographic record
Abstract
The capsids Sahlbergella singularis and Distantiella theobromae, the cocoa mosquito Helopeltis sp., and the green bug Bathycoelia thalassina are the main Heteroptera pests causing immense damage in cocoa fields in the department of Méagui, the main cocoa producing area of Côte d’Ivoire. The actual study was conducted to assess the spatial and temporal distribution of these pests in this department. From May 2020 to April 2021, the pest abundance and population dynamics were recorded once a month in seven selected cocoa farms in the localities of Yaodankro and Sérigbangan. Tarping and systematic search methods were used. The results indicated that the three types of insect pests are present and the abundance rates ranging from 20.83% to 42.22% from 15 910 individuals recorded. Capsids were more abundant in the Sérigbangan orchards than in those of Yaodankro, while the cocoa mosquito and the green bug were more abundant in Yaodankro than in Sérigbangan. The number of individuals remained relatively high throughout the year except in May-June (months of intense rainfall) where capsid and cocoa mosquito populations were less abundant. Peak populations occurred during the dry season (July, August, February, and March-April) and during the low/medium rainfall season (September and November). The cocoa mosquito and the green bug once considered minor pests were shown to be major pests.
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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 | 0.001 |
| 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) | 0.001 | 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".