Bibliographic record
Abstract
PANORAMEX — named after Panoramix, the druid character from the Asterix and Obelix comic strip — uses the RAVE UCT formula (Gelly and Silver, 2007) with UCB exploration constant 0 and the save-bridge pattern in simulations. PANORAMEX ran on an 18-node cluster of 4-core machines, using root parallelization and majority vote to select each move. This yielded about 6×10 5 simulations per second. WOLVE, the 2010 silver medallist (Arneson, Hayward, and Henderson, 2010) 2, uses truncated-width alpha-beta search, a Shannon-style electric circuit evaluation function with cell adjacencies augmented by virtual connections, and pruning of inferior cells. To save time, WOLVE uses a book built by caching 6-ply moves. This year Broderick Arneson added pondering and changed the search algorithm from fixed-ply to variable-ply with timemanaged iterative deepening. WOLVE used 2 threads (one to select moves, one to solve) on a 4-core machine, reaching 6-ply on most moves. MOHEX, the 2010 gold medallist (Arneson et al., 2010), is a Monte Carlo tree search program built on the code base of FUEGO, the Go program developed by Martin Müller, Markus Enzenberger and others at the University of Alberta. FUEGO uses lock-free parallelization (Enzenberger and Müller, 2009), and backs up virtual losses for better parallelization. MOHEX computes virtual connections and inferior cells in UCT tree nodes visited at least 400 times. This year Arneson added pondering, Huang helped with tuning, and Pawlewicz improved the
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.014 |
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".