Bibliographic record
Abstract
In many problems in commonsense reasoning and intelligent manufacturing, we need to reason about cutting, joining, and folding sheets of materials such as cardboard and metal. In this paper we introduce a first-order ontology of shape (called BoxWorld) that can support these applications. We reuse an existing ontology of shape for object recognition with 2D shapes (called CardWorld) and extend the axioms to three-dimensional shapes in the BoxWorld Ontology. A distinguishing characteristic of these ontologies is that they use only the notions of incidence and betweenness rather than Euclidean geometry as axiomatized by Hilbert and Tarski. Prelude When Alice went to the kitchen for her breakfast cereal, she opened the box by removing the tab from the slot at the top of the box. Discovering it empty, she reached for a new box, which she opened by detaching one side of the lid from the other. She closed the box by opening the slot on one slide of the lid and then inserted the tab into the slot. After breakfast, she disassembled the old box and dropped it into the recycling bin.
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 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 | 0.000 |
| 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) | 0.000 | 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 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".