Categorying Sugarcane Production Based On Factors Affecting Productivity With The K-Nearest Neighbor Algorithm
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
Sugarcane (Saccharum Officanarum is an annual plantation crop, which has its own characteristics, because the stem contains sugar. To classify the results of sugarcane production, currently still using the manual method by only looking at the current conditions of sugarcane production. This is less efficient because there is no calculation process in grouping sugarcane. So that mistakes can occur in grouping sugarcane production to get good results or not in the assessment of sugarcane grouping at PTPN II Kwala Madu. For this reason, the author will create an alternative application system that can group sugarcane production with the K-Nearest Neighbor algorithm to find out the best type of sugarcane production based on the factors. The application made by the author uses the PHP programming language and uses the MySQL database as data storage. The system is made as easy as possible to make it easier for users to use and understand later.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it