Predicting the rates of cross-pollination between GM and non-GM crops using RBFNN with SVM and bootstrap approach
Bibliographic record
Abstract
Recently, genetically modified (GM) technology has been successfully used to reduce the cost and to enhance the profit in agriculture. Although GM technology brings many benefits for non-food crops, people still misgive the effects of GM products for the health and the environment. Furthermore, GM crops might affect food (non-GM) crops in the open environment. Hence, how to find strategies for the coexistence of GM and non-GM crops are become a popular issue. One of the strategies is to determine a befitting distance between GM and non-GM crops to reduce the cross-pollination occurred by predicting the rate of cross-pollination. Owing to most of the existing methods for predicting the cross-pollination rates of non-GM crops are only based on the distance between GM and non-GM crops. To counter this problem, we propose a hybrid method, which is composed of radial basis function neural network (RBFNN), support vector machine (SVM) and bootstrap, to apply in this issue. The proposed method includes three specificities. (a) The proposed method reduces the effect of imbalance class problem. (b) The proposed method uses more variables, which are effect the cross-pollination rates, for prediction to enhance the prediction accuracy. (c) The proposed method searches relevant samples to reduce execution time and enhance the prediction accuracy. The results show the performance of our method is better than the existing methods in terms of the root mean square error (RMSE) in prediction and the correlation coefficient between the actual and the predicted cross-pollination rates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".