Analysis of Hornet Forecast Model based on Fuzzy Theory
Why this work is in the frame
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Bibliographic record
Abstract
The 14 Positive ID the paperre arranged in time order and the GM Model was used to predict the range of propagation, and the results of the prediction the paper re obtained as follows: from 48.92 to 49.05 in length and from -122.47 to -122.55 in latitude in 2021. There is a distance of 30 km betthe paperen the predicted results and the initial point where the presence of hornets was confirmed. The average relative error is less than 0.01, so the model prediction accuracy is good. Since the life cycle of hornets is very related to seasons, the time is converted into seasons and then One-Hot-Encoding of seasons; the TFIDF Algorithm is used to calculate the importance of each Note to replace the original Notes. The SMOTE Method used in this paper to fill the Positive ID minority class sample leads to the proliferation of Vespa mandarinia seriously endangering the local ecology, so the SMOTE Method used in this paper to fill the Positive ID minority class sample. The models all seek to maximize the recall of a few classes of Positive ID. After model testing our models are all excellent in identifying pests accurately, as evidenced by the ROC (with Positive ID as a positive example) curve and AUC =0.99.
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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 | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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