Detecting outliers in irregularly distributed spatial data sets by locally adaptive and robust statistical analysis and GIS
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
Abstract In this paper, we propose a new method for detecting outliers in an irregularly distributed spatial data set. Our method has two desirable properties. First, it is functionally effective due to the introduction of sensitive outlier indices and locally adaptive and robust statistical criteria. Second, it is computationally efficient because of the use of super-block based spatial data sorting and searching scheme. Our method has been implemented using the C programming language and integrated with the Arc/Info GIS system. The integration leads to a powerful exploratory data analysis tool for checking and analysing anomalous values in a GIS environment. Local outliers can be automatically labeled with our method, subject to some user-defined parameters. Outliers represent anomalous or suspicious values in a statistical sense, which may not necessarily be erroneous values. Instead of being simply discarded, statistical outliers should be investigated further using prior qualitative knowledge or in association with other GIS data layers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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