Varietal Susceptibility to Sternochetus mangiferae (Fabricius) (Coleoptera: Curculionidae) and Consequences for Mango production in Northern Côte d’Ivoire
Bibliographic record
Abstract
In northern Côte d’Ivoire, mango production is significantly hampered by poor fruit quality, largely due to insect pest infestations in orchards. Among these pests, the mango stone weevil (S. mangiferae) is a major cause of internal fruit damage. To support efforts to improve mango quality, a study was conducted from February to July 2023 in the Poro, Bagoué, and Tchologo regions, aiming to identify mango varieties more susceptible to weevil infestation and potentially contributing to its proliferation. Five mango varieties were evaluated: Kent, Keitt, Brooks, Amélie, and the local cultivar known as Lowô. For each variety, five mango fruits were exposed to 20 adult weevils in controlled tubs, with a total of 47 replicates performed (a total of 235 mangoes). Additionally, to assess weevil-induced fruit drop, 10 Amélie mango trees were monitored across six orchards in the three regions. Each week, 200 fallen mangoes were collected along five transects beneath each tree and dissected to detect and quantify internal infestation. Results revealed that the local variety (Lowô) was the least preferred by S. mangiferae, while other varieties showed varying degrees of susceptibility. Weevil-induced fruit drop was closely linked to the fruit’s phenological stage and reached levels as high as 64% in some cases. The findings suggest that promoting less susceptible local varieties and implementing systematic removal of fallen infested fruit could serve as effective integrated pest management strategies. These approaches may contribute to reducing weevil populations and enhancing mango fruit quality in northern Côte d’Ivoire.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".