Topological Similarity Measurement of Region Compositions Based on Boundary Contacts
Bibliographic record
Abstract
Topological relationship, one of the most important subjects in spatial analysis, has been studied thor oughly and applied to various fields. An important application of topological relations is the similarity measurement on spatial scenes. However, when considering the common topological relations, there exists dif ficulty in distinguishing different region compositions where complicated boundary contacts occur between regions. To address this problem, a method of describing detailed topological relations in a region compo si tion has been proposed [Lewis et al. 2013], by traversing a region's boundary and recording boundary intersections one by one. To implement similarity measurement on region compositions where complex boundary contacts happen, we propose a measuring model based on the boundary contact recording method. The model is struc tured in two steps, a preliminary matching step and an exact matching step. In the preliminary matching step, we recognize and filter the very dissimilar candidates comparing to the reference region composition; meanwhile, we obtain corresponding relations of regions between the potential candidates and the reference, and represent the correspondences with an association graph composed of nodes and edges. In the exact matching step, we encode boundary contact records to binary sequence, and adopt a sequence alignment method, an approach from bioinformatics to compare the sequences of DNA, RNA, or protein, to fulfill the topological similarity measurement for two region compositions. We illustrate the complete process of our model through a case study, and show the survey result on weight setting for criteria in the exact matching step.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".