Spatial Analysis of Point and Quadrat Data
Bibliographic record
Abstract
The spatial patterns of point events in the plane can exist at several different scales in a single data set. The assessment of point patterns can be based on the distances between neighbour events, on the counts of events in quadrats or on counts of events in point-centred circles of changing size. Ripley’s K function evaluates simple point patterns and can be modified for different spatial dimensions, for bi- and multi-variate variables and for non-homogeneous data. Quadrat-based quantitative data are usually analysed by one of many related ’quadrat variance’ methods that assess variance or covariance as a function of spatial scale and which can also be modified for different conditions, such as bi- or multi-variate data. There are related methods from other traditions to be considered, including spectral analysis and wavelets. These approaches share a conceptual basis of comparing the data with spatial templates and we provide a summary of their relationships and differences.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".