Bibliographic record
Abstract
We identify the type of wheat seeds into three categories: Kama, Rosa, and Canadian, different species of wheat in the real world. To do this, we analyze features including the wheat seed’s perimeter, area, compactness, length, width, asymmetry coefficient, and groove length. Then, we transform the variables of the features into machine-readable numbers. With the data being correctly altered, Machine learning methods, including Standard Scaler, train_test_split, prepare the data by normalizing the data units, and split the data into training and testing sets to train and test a model. Finally, Categorical Classification is used to create a model that classifies wheat seed varieties. The five hundred times of iterations and tests of the model resulted in a 97% accuracy and a 0.13 loss, indicating a good performance. This model allows farmers without a huge experience in wheat identification to guess if they are examining Kama, Rosa, and Canadian wheat seeds.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".