Diagnosa Penyakit pada Jamur Tiram Putih menggunakan Metode Dempster Shafer
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
: Oyster mushrooms are one of the popular horticultural products in the community, with a significant increase in demand from year to year. However, farmers often face difficulties in identifying and preventing diseases that attack oyster mushroom plants, which have an impact on production stability. To overcome this problem, this study aims to design an expert system that can diagnose diseases in white oyster mushrooms using the Dempster Shafer Method. This system is designed to provide accurate diagnostic information and solutions to deal with diseases in oyster mushrooms, so as to improve the quality and quantity of production. This study also strengthens previous studies using the Certainty Factor method, with an emphasis on the use of expert knowledge to overcome disease problems in oyster mushroom cultivation. The case study was conducted at Omah Jamur Tiram Stabat, where the results of the implementation are expected to increase the effectiveness and efficiency of oyster mushroom cultivation in the area.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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