Extended Subtree: A New Similarity Function for Tree Structured Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although several distance or similarity functions for trees have been introduced, their performance is not always satisfactory in many applications, ranging from document clustering to natural language processing. This research proposes a new similarity function for trees, namely Extended Subtree (EST), where a new subtree mapping is proposed. EST generalizes the edit base distances by providing new rules for subtree mapping. Further, the new approach seeks to resolve the problems and limitations of previous approaches. Extensive evaluation frameworks are developed to evaluate the performance of the new approach against previous proposals. Clustering and classification case studies utilizing three real-world and one synthetic labeled data sets are performed to provide an unbiased evaluation where different distance functions are investigated. The experimental results demonstrate the superior performance of the proposed distance function. In addition, an empirical runtime analysis demonstrates that the new approach is one of the best tree distance functions in terms of runtime efficiency.
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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 | 0.002 |
| Open science | 0.002 | 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 it