Distribution Estimation of Invasive Species Based on Crowdsourcing Reports
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
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.
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) | 0.084 | 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