Tree Height and the Asymptotic Mean of the Colijn–Plazzotta Rank of Unlabeled Binary Rooted Trees
Bibliographic record
Abstract
Abstract The Colijn–Plazzotta ranking is a bijective encoding of the unlabeled binary rooted trees with positive integers. We show that the rank f ( t ) of a tree t is closely related to its height h , the maximal path length from a leaf to the root. We consider the rank $$f(\tau _n)$$ f ( τ n ) of a random n -leaf tree $$\tau _n$$ τ n under each of three models: (i) uniformly random unlabeled unordered binary rooted trees, or unlabeled topologies; (ii) uniformly random leaf-labeled binary trees, or labeled topologies under the uniform model; and (iii) random binary search trees, or labeled topologies under the Yule–Harding model. Relying on the close relationship between tree rank and tree height, we obtain results concerning the asymptotic properties of $$\log \log f(\tau _n)$$ log log f ( τ n ) . In particular, we find $${\mathbb {E}}\{\log _2 \log f(\tau _n)\} \sim 2 \sqrt{\pi n}$$ E { log 2 log f ( τ n ) } ∼ 2 π n for uniformly random unlabeled ordered binary rooted trees and uniformly random leaf-labeled binary trees, and for a constant $$\alpha \approx 4.31107$$ α ≈ 4.31107 , $${\mathbb {E}}\{\log _2 \log f(\tau _n)\} \sim \alpha \log n $$ E { log 2 log f ( τ n ) } ∼ α log n for leaf-labeled binary trees under the Yule–Harding model. We show that the mean of $$f(\tau _n)$$ f ( τ n ) itself under the three models is largely determined by the rank $$c_{n-1}$$ c n - 1 of the highest-ranked tree—the caterpillar—obtaining an asymptotic relationship with $$\pi _n c_{n-1}$$ π n c n - 1
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".