Big Data Analysis and Calculation in the Prediction of Invasion of Vespa Mandarinia
Bibliographic record
Abstract
In late 2019, Vespa Mandarinia invaded the US state of Washington from Canada. Meanwhile, there were thousands of unconfirmed sightings of Vespa Mandarinia, including dates, locations, pictures and descriptions. In order to help the Washington State Government more effectively control the Vespa Mandarinia intrusion. We built a model to predict the intrusion trend of Vespa Mandarinia and analyse whether the reports submitted by the public are valid. On the basis of the linear correlation coefficient of time and propagation distance, we used particle swarm optimization algorithm to get the initial position of invasion. Since the accuracy of the eyewitness reports provided by the public was uneven, we adopted a comprehensive evaluation model of score, and used three variates, including Distance score, Text score and Image score, to evaluate the credibility of eyewitness reports. Prioritizing major eyewitness reports was finally determined. Finally, the optimal time interval for updating the evaluation model is determined by calculating the linear correlation coefficient. As a result, we have a mathematical model that can effectively simulate biological invasions of Vespa Mandarinia and determine whether eyewitness reports are credible. It can be an effective tool for biological invasion control in Washington State. In this paper, the trend of biological invasion and the credibility of eyewitness reports are quantitatively described by mathematical model tools, which provides new ideas and methods for biological invasion control.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".