SpatialBoundaries.jl: edge detection using spatial wombling
Bibliographic record
Abstract
Spatial wombling is an approach for detecting edges within a defined two‐dimensional landscape. This is achieved by calculating the rate and direction of change through the interpolation of points. This not only gives an approximation as to the shape of the landscape but can also be used to identify candidate boundaries cells that delimit a shift from one state to another within the landscape. Here we introduce the SpatialBoundaries.jl package for Julia, which has been developed to implement the wombling algorithm for datasets that are spatially referenced for both uniformly or randomly sampled landscapes. From a practical perspective, the wombling functionality allow the user to answer two questions: how much and in which direction does the variable of interest change within the landscape? Whereas the boundaries functionality identifies candidate boundary cells. We conclude by providing a working example of the package using the various woody plant layers for Britain and Ireland from the EarthEnv database.
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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 | 0.001 |
| 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) | 0.001 | 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 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".