Bibliographic record
Abstract
A bstract . We describe the generation of a library of eyes surrounded by only one chain which we call monolithic eyes. Apart from applying the library in the life-and-death program G o T oots it also can be used as a source for the study of unusual positions in Go as done in the second half of the paper. Introduction In using principles of combinatorial game theory it has been discussed in the literature how in the game of Go one can assign values to eyes in order to decide whether a position lives unconditionally, simply by adding these values and checking whether or not their sum reaches the value of two (see Landman). These concepts are applied in computer Go programs (as in) and a computer generated library of eye shapes is available from Dave Dyer. In this contribution we describe the generation of a library of eyes surrounded by only one chain which we call monolithic eyes. Compared to Dyer's library our database has a number of extensions: an evaluation of the number of ko threats needed to live or to kill the eye, the consideration of a larger number of external liberties and of an extra attached eye, larger eye sizes, the determination of all winning moves and others. In the following section we describe a procedure to bring any set of empty or occupied points into a unique position by using shifts and symmetries of the board.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.017 |
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".