Spatial Outlier Detection Based on Delaunay Triangulation
Why this work is in the frame
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Bibliographic record
Abstract
Spatial outliers represent objects whose non-spatial attribute values are significantly different from the values of theirs spatial neighborhoods,in order to mining the valuable outliers,spatial outliers mining should consider the special characteristics of spatial data.The major drawbacks of existing methods are that spatial characteristics aren't considered fully and they use k-neighborhood method to define neighborhood which depends on a priori given parameters.So we propose a new neighborhood-defined algorithm and compute the spatial weighted matrix which is based on Delaunay Triangulation.In virtue of that,the spatial outliers detection based on Delaunay Triangulation is proposed.In addition,using a real-world ecological geochemical dataset,we demonstrate that our approach is efficient and has lower human dependence.
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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 | 0.001 |
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 it