Predicting Mango Sudden Decline Due to Ceratocystis fimbriata Under a Changing Climate
Bibliographic record
Abstract
Mango fruit trees are an important fruit crop due to their high value. Mango sudden decline (MSD) is a major disease that threatens mango trees in Oman and worldwide. The objective of this study was to identify those areas in northern Oman in which Ceratocystis fimbriata (a plant fungal pathogen causing MSD) may establish itself under various climate change scenarios. The MaxEnt model used in this study was based on data for the period 1970-2000 and then projected to future climate periods. This study modeled the future distribution of C. fimbriata for 2021–2040, 2041–2060, 2061–2080, and 2081–2100 climatic scenarios. Fifteen affected locations and seven bioclimatic variables were investigated in this study. The model showed values between 0.896 and 0.913 (habitat suitability) which represented a good model outcome. The jackknife test showed that the mean diurnal range in temperature, precipitation of the driest month, and elevation contributed to C. fimbriata distribution. From 2021 through 2040, a total area of 1,889 km2 was found to be highly suitable for C. fimbriata in Northern Oman. Compared with the 2021–2040 period, the poorly suitable area would increase in both 2041–2060 and 2081–2100 periods. The moderately suitable regions for C. fimbriata would decrease under all scenarios investigated. However, the total area of the suitable areas, with all scenarios, would increase, except during the 2041-2060 period. This research offers a tool to better manage and prevent the possible Ceratocystis blight (C. fimbriata) and bark beetle (Hypocryphalus mangiferae) invasions under future projected climatic scenarios. Keywords: Mango sudden decline (MSD), “Ceratocystis fimbriata”, bioclimatic variables, climate change, Sultanate of Oman, Maxent.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".