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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".