Migdolus fryanus Damage Causes Decrease in the Starch Content in Manihot esculenta
Bibliographic record
Abstract
The beetle M. fryanus causes serious damage to cassava in Brazil. However, due to the subterranean behavior of the larvae and the recent appearance of this pest in cassava, little is known about the main behavioral characteristics and damage caused to this crop. The main aim of this study is to demonstrate the variation of starch production in the roots of M. esculenta as a result of the intensity of damage caused by M. fryanus. The study was carried out in a commercial cassava farm in Paraná, Brazil. The proposed scale for damage was: (1) No apparent root damage; (2) roots with scrapings; (3) roots with scraping across the cortex; (4) damaged roots and galleries; (5) roots with galleries and presentation of rot. The parameters evaluated were the damage caused by M. fryanus in roots and starch content, estimated by using a hydrostatic scale, starch extracted by cassava processing, and the starch reduction percentage in damaged roots, assessed by using the hydrostatic scale (SC) and processing methods (SCP). It was observed that there was manifestation of damage in approximately 60% of the roots collected directly from the ground, with levels representing loss of starch produced by the plant. Decreases in the productive parameters, such as starch content, were measured according to the increase of the proposed damage levels in the two cassava cultivation cycles, with a starch reduction rate higher than 20% when the roots suffered the most severe damage.
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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".