Distribution Estimation of Invasive Species Based on Crowdsourcing Reports
Bibliographic record
Abstract
Species invasion will cause certain harm to the local ecosystem.Vespa mandarinia, discovered on Vancouver Island, is harmful to agriculture and predators of European honeybees.The government tried to use a crowdsourcing system to collect information and formulate policies to eliminate Vespa mandarinia.However, the information provided by the local population about Vespa mandarinia is not entirely accurate.For this problem, we build a method to mine trusted information in massive crowdsourcing Vespa mandarinia reports.We consider providing the date and location of the report, and establishing a credibility calculation model for further analysis.For the report date, we calculate the normal distribution parameters based on the frequency of the report in each season to measure the reliability of a single report.For report location, we use K-means cluster analysis to find the location of the center point, which is regarded as a hive, count the report points in each hive radiation range, and use these points to generate two-dimensional normal distribution parameters to normalize the data and eliminate statistical errors.We take the probability density of the report at its location as the reliability of the reports.Through credibility, we can screen out reports that are more likely to be positive for prioritizing investigation.In order to better analyze the newly discovered reports in the future and ensure the timeliness of the model, we set up distributed incremental adjustment model to modify normal distribution parameters, and update the existing model.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".