parspatstat: An R Package for Large-Scale Spatial Analysis with Parallel Computing
Why this work is in the frame
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Bibliographic record
Abstract
The parspatstat package is an extension of the spatstat package for spatial anal-ysis. It implements some of the more common functions of spatstat in a parallel environment using theRmpi implementation of the message passing interface (MPI) framework. Spatial descriptive statistics such as Ripley’s K-function have wide ap-plicability in spatial analysis but current implementations do not scale well for large data sets. Parallel computing (high performance computing) is one solution that can provide almost linear scalability to these applications. The usages of these functions are kept as similar as possible to the current spatstat function to aid in updating existing algorithms. Implementation, optimizations, and complications that arise from parallelizing existing algorithms are discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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 it