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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it