Landmarks in Binary Tree Derived Architectures.
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
Let M = {v1, v2 ... vl} be an ordered set of vertices in a graph G. Then (d(u, v1), d(u, v2) ... d(u, vl)) is called the M-location of a vertex u of G. The set M is called a locating set if the vertices of G have distinct M-locations. A minimum locating set is a set M with minimum cardinality. The cardinality of a minimum locating set of G is called Location Number L(G). This concept has wide applications in motion planning and in the field of robotics. In this paper we consider networks with binary tree as an underlying structure and determine minimum locating set of such architectures.We show that the location number of an n-level X-tree lies between 2 −3 and 2 −3 + 2. We further prove that the location number of an n × n mesh of trees is greater than or equal to n/2 and less than or equal to n.
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.
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 it