Bibliographic record
Abstract
Introduction to the ‘lotto’ game ‘Lotto’ is among the most popular games offered by state lottery associations accounting for roughly one-quarter of total revenues for state-run US lotteries in the late 1990s and early 2000s. As of August 2004, forty states had state-run lotteries, and every state with a lottery offered some version of a lotto game either through their own game or through a multi-state association such as the twenty-seven-state Multi-State Lottery Association (Powerball) or the eleven-state Big Game/Mega-Millions association. Lotto games generally consist of an individual picking a set of five or six numbers from a group of approximately 35–55 choices. Winning numbers are then randomly selected at a weekly or bi-weekly drawing. A player whose ticket matches all of the winning numbers wins the jackpot prize, which is funded by allocating a percentage of ticket sales to the jackpot prize pool. Players matching some but not all of the winning numbers win smaller consolation prizes. If more than one ticket matches all the numbers, the money in the fund is divided evenly among the number of winning tickets while if no ticket matches the winning numbers, the money in the fund is carried over into the next drawing and is added on to the allocated funds from ticket sales in the next period. Because the jackpot prize fund is allowed to roll-over in this manner, the jackpot prize can become quite large if no one hits the jackpot in a large number of successive periods.
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 | 0.002 | 0.003 |
| 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 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".