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
SummaryIn the simple version of Skunk, a pair of dice is rolled again and again until either you choose to sit or at least one 1 comes up. If you sit, your payoff is the dice sum of all your previous rolls. If at least one 1 comes up while you are still standing, your payoff is zero. Here we look at an extension of the game in which you must also choose the number of dice to use and you can alter this from round to round. The simple game seems to have a straightforward enough optimal strategy but our analysis of the extension reveals flaws in our initial reasoning. Some additional extensions are considered. Additional informationNotes on contributorsDavid KongDAVID KONG is a 3rd year undergraduate student at the Queen's School of Business and is pursuing a Bachelor of Science in mathematics. He has worked in both the hedge fund and private equity industries (Fore Research, ONCAP Partners) and was invited to the 2010 Canadian Mathematics Olympiad. This has led to an interest in statistics, probability, and modeling, particularly through EXCEL. David enjoys skiing, biking, cooking, and creating latte art.Peter D. TaylorPETER TAYLOR is a professor in the Department of Mathematics and Statistics at Queen's University, cross-appointed to the Department of Biology and the Faculty of Education. He is a Queen's graduate and has a Harvard Ph.D. His area of research is theoretical evolutionary ecology, particularly the evolution of cooperative behaviour. He is a 3M Teaching Fellow and is Chair of the Education Committee of the Canadian Mathematical Society. Recently he chaired the Task Force at Queen's, charged with writing the university's new Academic Plan.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it