Morphological Character Analysis and Signal Cryptic Speciation in Lasiodiplodia theobromae on Cashew (Anacardium occidentale L.)
Bibliographic record
Abstract
Lasiodiplodia theobromae (Pat) Griffon & Maubl. is a pathogen causing inflorescence dieback disease of cashew in Nigeria and also a common pathogen with a wide host range in the tropics and sub-tropics. The character variations in this pathogen necessitate better understanding of it towards development of management strategies. Isolates identified as L. theobromae were cultured from inflorescence dieback disease of cashew across growing ecologies of Nigeria and studied base on morphological characters. Variability in mycelial texture and colour, conidia and septa sizes and pycnidia production were recorded in this study. The Principal Component Analysis (PCA) and WARD clustering analysis identified four well-supported traits within the isolate group. Isolates within each cluster was: 2, 2, 4 and 1 respectively and isolate CDA1416 (Obollo-Afor) and CDA2924 (Idi-Ayunre) in cluster III were the most similar. Members within clusters I and II united at the semi-partial R-Square distance of 0.0294 and 0.0278 respectively. Isolate CDA2308 (Oro) was distinguished among others and signal a potential cryptic specie, differences in these isolates were supported by conidial morphology and textural variations. This understanding will form the bases for development of diseases management strategy against the pathogen.
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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 | 0.000 |
| 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) | 0.000 | 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 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".