Semantic computing of simplicity in attributed generalized trees
Bibliographic record
Abstract
In earlier work, Attributed Generalized Tree (AGT) structures, having vertex labels, edge labels, and edge weights have been introduced. AGTs can represent knowledge in domains containing rich semantic/pragmatic object-centered descriptions as well as complex relations between objects. Therefore, AGTs have applications in many domains such as health, business, and finance (e.g., insurance underwriting). In this paper, we introduce a function to quantify the simplicity of an arbitrary AGT. Our simplicity function takes into account branch, position, and weight factors; it maps the structure to a value in the interval [0,1]. The recursive simplicity algorithm performs a top-down traversal of the AGT and computes its simplicity bottom-up. Characteristic properties of the AGT simplicity measure are analyzed, and AGTs in a test dataset are ranked based on their simplicity values computed using our simplicity algorithm. The experimental analysis confirms our expectation that the simplicity value decreases with increasing the complexity of AGT structure.
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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 | 0.000 |
| 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".