Behavior Prediction of Vespa mandarinia based on Convolutional Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Vespa mandarinia poses a significant threat to honey bees and beekeepers. This paper aims to accurately predict the presence and behavior of Vespa mandarinia to enable effective control strategies. We develop an "Asian hornet queen prediction model" to forecast the dispersal path and number of queens, identifying their concentration in Bellingham, Washington, and Bellingham, Canada. Using a convolutional neural network, we achieve 96.13% accuracy in identifying Vespa mandarinia images. Analyzing confidence levels of unprocessed and unverified labels reveals a significant number of high-confidence samples. Incorporating human control factors into the model, we find 60% human intervention to be most effective in reducing the number of Asian hornet queens. Ultimately, our research highlights the potential for anthropological or ecological measures to eliminate Vespa mandarinia populations, aiding in resource allocation for proactive management.
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.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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