Bibliographic record
Abstract
In the game of Skunk a pair of dice is rolled again and again and as long as you remain “standing” you can keep adding the totals to your score. At any time you can “sit” and then you take home what you have won. However if you are standing and a “one” comes up on either die, the game is over and you lose everything. An optimal strategy is traditionally developed by comparing the expected score of standing with the expected score of sitting. As long as E(standing) > E(sitting), you would continue to roll the dice. As you accumulate points, you begin risking more points. At one point it becomes too risky to go forward. However, we found this traditional methodology to be flawed (though the answer remains the same). This solution focuses only on the expected score in the next roll, instead of factoring in the expected total score that can be gained over indefinite future. The error does not come to light until weanalyze a variation where you are also allowed to choose the number of dice to use. All the equations (using the idea E(standing) > E(sitting)) we solved led us down a misleading and often intractable road. Yet, through reasoning, we figured that there is no strategy better than throwing 1-dice at a time. Proving this is quite difficult, because it had to be shown that the pure 1-dice strategy is better than all of the other (infinite) strategies, since the game can go on forever.
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 | 0.024 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.009 | 0.026 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".