Role of stressed mango host conditions in attraction of and colonization by the mango bark beetle<i> Hypocryphalus mangiferae</i> Stebbing (Coleoptera: Curculionidae: Scolytinae) and in the symptom development of quick decline of mango trees in Pakistan
Bibliographic record
Abstract
Abstract The mango sudden death syndrome has become a serious threat to the mango industry and caused significant decline in mango production worldwide. The bark beetle Hypocryphalus mangiferae (Stebbing) (Coleoptera: Curculionidae: Scolytinae) has been suggested as a potential vector of the disease based primarily on field observations with little or no supporting empirical data. In this study, we investigated the role of infected mango trees in host attraction and colonization by H. mangiferae to determine if beetle attack and colonization contributes to the disease progression on mango trees. Initially, the role of various stress factors on beetle attraction and disease progression was assessed under lathe house conditions from 2008 to 2009. Results suggest that symptomatic or recently inoculated mango trees (without any obvious symptoms) are preferentially colonized by H. mangiferae. Although not significant, high numbers of beetles attacked stressed or wounded mango trees, compared to healthy or dead mango trees. Disease symptoms after beetle colonization, such as bark splitting, wilting and oozing, were further evaluated. These symptoms showed positive correlation with the degree of disease severity and host plant condition. Furthermore, two fungi, Ceratocystis fimbriata and Lasiodiplodia theobromae, were frequently isolated from the beetle and beetle‐colonized trees. Based on these findings, they suggests that H. mangiferae can vector multiple fungi associated with mango sudden decline disease and play a significant role in outbreaks of this disease.
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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) | 0.001 | 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".