How Can Geostatistics Help Us Understand Deep Learning?
Bibliographic record
Abstract
Convolutional neural networks have shown excellent performance in image processing. However, due to the black-box property of deep networks in applications, people still doubt its credibility, which has led to the birth of many interpretation methods for computer vision networks, but their interpretation effects can only be subjectively assessed by human vision on the heatmaps generated by these interpretation methods. In this paper, we propose a novel Geospatial Analysis for eXplainable Artificial Intelligence (GeoSeXAI) method, which for the first time introduces the spatial autocorrelation analysis method in geostatistics combined with the XAI algorithm and the SAR images to provide an accurate and objective quantitative assessment of the spatial distribution of the XAI attribution, as well as a global and objective assessment of the XAI attribution. global objective quantitative assessment, and provides a local qualitative explanation that is easier for human visual understanding.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".