Bibliographic record
Abstract
A bstract . Bagha Chal, or “Moving Tiger”, is an ancient Nepali board game also known as Tigers and Goats . We briefly describe the game, some of its characteristics, and the results obtained from an earlier computer analysis. As in some other games such as Merrill's, play starts with a placement phase where 20 pieces are dropped on the board, followed by a sliding phase during which pieces move and may be captured. The endgame sliding phase had been analyzed exhaustively using retrograde analysis, yielding a database consisting of 88,260,972 positions, which are inequivalent under symmetry. The placement phase involves a search of 39 plies whose game tree complexity is estimated to be of the order 10 41 . This search has now been completed with the help of various optimization techniques. The two main ones are: confronting a heuristic player with an optimal opponent, thus cutting the search depth in half; and constructing a database of positions halfway down the search tree whose game-theoretic value is determined exhaustively. The result of this search is that Tigers and Goats is a draw if played optimally. Introduction Bagha Chal, or “Moving Tiger”, is an ancient Nepali board game, which has recently attracted attention among game fans under the name Tigers and Goats . This game between two opponents, whom we call “Tiger” and “Goat”, is similar in concept to a number of other asymmetric games played around the world–asymmetric in the sense that the opponents fight with weapons of different characteristics, a feature whose entertainment value has been known since the days of Roman gladiator combat.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".