Expert System To Diagnose Stem Border In Sugarcane Plant With Certainty Factor Method
Bibliographic record
Abstract
Sugarcane (Saccharum Officanarum L) is an annual plantation crop, which has its own characteristics, because it contains sugar in its stems. Sugarcane belongs to the grass family (graminae) like rice, reeds, corn, bamboo and others. One of the problems that exist in sugarcane plantations at PT Perkebunan Nusantara II is stem borer pests. Losses due to borer attacks can be in the form of a decrease in sugarcane weight, yield and quality of the sap obtained. pests on sugarcane plants cause a decrease in sugar production of about 10%. There are three types of stem borer pests found in PT Perkebunan Nusantara II, namely shoot borer pests, striped stem borer pests, and giant stem borer pests. Meanwhile, biological control is by spreading natural predators of pests which will suppress the population of widespread distribution and breeding of pests which are commonly called parasitoids. Parasitoids will become predators of their respective hosts, so appropriate diagnostic therapy for stem borer pests is needed so that the spread of stem borer pests does not become more widespread and the parasitoids to be spread operate effectively. By using the Certainty Factor method, you can find out the types of pests that are attacking sugarcane plants. Based on manual calculations, the dipole yield was 79% on one of the selected pest types based on the symptoms induced in the sugarcane plant.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".