Application of Weighted Product (WP) Method in Selection of Superior Seed Varieties of Sugar Cane
Bibliographic record
Abstract
Sugarcane (Saccharum officinarum Linn) is a raw material for sugar production. PT. Perkebunan Nusantara II is one of the nurseries and sugarcane processing places in North Sumatra. The results of observations on sugarcane production are always increasing but the results are not too optimal. Determination of superior varieties of sugarcane seeds is very appropriate to be one of the factors supporting the development of sugarcane production so that there is no longer sugarcane milling period and low yields which can lead to less than optimal sugar production. To overcome this, it is necessary to build a system that can help determine superior seed varieties in sugarcane. The Weighted Product (WP) method in a decision support system is a method of completion by using multiplication to link the attribute rating, where the attribute rating must be raised first with the weight of the attribute in question. This decision support system with the WP method was created to assist in the selection of high-yielding sugarcane varieties.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".