A new method to measure spatial association for ecological count data
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
A new method is introduced to assess the spatial association between two sets of count data. This features a measure of local association for counts, defined for each sample unit. The new measure is based on a comparison of the spatial SADIE clustering index of the two sets at each sample unit; the mean of the measure is represented by the simple correlation coefficient between the clustering indices of the two sets. The randomization method allows the construction of a test and critical values. For the first time, spatial association may be mapped for count data; clusters of units with positive association or negative dissociation may be identified. The method is exemplified by analysis of spatial pattern and spatial association of counts of male and female tupelo trees from three plots in a South Carolina swamp forest. In addition, methods are presented to distinguish larger-scale apparent association between the sexes, caused by indirect effects, from direct smaller-scale association. No tendency was found for the sexes to occur together at the small-scale, only an apparent affinity caused through their co-location in particular subareas of each plot. The conversion from mapped to count data requires a choice of unit size; the conclusions of these analyses were not affected greatly by changes in unit size.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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