An Incremental and Philosophically Different Approach to Measuring Raster Patch Porosity
Bibliographic record
Abstract
A new method for measuring the porosity of individual 2D raster patches in a GIS for characterizing the combined complexity of a shape’s edge in conjunction with its internal perforations is developed. The method is centered on comparing the number of cellular edge–edge joins relative to the theoretical maximum number of similar joins possible given a set number of cells comprising a landscape patch. As this porosity (Φ) increases, the patch (or shape) can be viewed as deviating from a maximally compact form, comprising higher edge complexity and internal heterogeneity (inclusion of perforations). The approach is useful for characterizing shapes for which a simple perimeter- or area-based metric misses the internal complexity and where the porosity of the patch may provide insight into spatial processes leading to the development of the landscape fabric. I present theoretical results to illustrate the mechanics of the approach and a small case study of boreal wildfire residual vegetation patches in Ontario, where real resulting wildfire process-driven landscape patches are assessed for their porosity at five spatial resolutions. The results indicate that naturally occurring and unsuppressed boreal wildfires in the study area typically produce residual vegetation patches with an average porosity of 17.6%, although this value varies slightly with the spatial resolution of the data representation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".