Bibliographic record
Abstract
Qualitative Spatial Reasoning (QSR) is an alternative technique to represent spatial relations \nwithout using numbers. Regions and their relationships are used as qualitative terms. Mostly \npeer qualitative spatial reasonings has two aspect: (a) the first aspect is based on inclusion \nand it focuses on the ”part-of” relationship. This aspect is mathematically covered by \nmereology. (b) the second aspect focuses on topological nature, i.e., whether they are in \n”contact” without having a common part. Mereotopology is a mathematical theory that \ncovers these two aspects. \nThe theoretical aspect of this thesis is to use classical propositional logic with non-classical \nrelevance logic to obtain a logic capable of reasoning about Boolean algebras i.e., the \nmereological aspect of QSR. Then, we extended the logic further by adding modal logic \noperators in order to reason about topological contact i.e., the topological aspect of QSR. \nThus, we name this logic Modal Relevance Logic (MRL). We have provided a natural \ndeduction system for this logic by defining inference rules for the operators and constants \nused in our (MRL) logic and shown that our system is correct. Furthermore, we have used \nthe functional programming language and interactive theorem prover Coq to implement \nthe definitions and natural deduction rules in order to provide an interactive system for \nreasoning in the logic.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".