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
The “RollerCoaster Tycoon” video game involves creating rollercoaster tracks that optimize for various game metrics while also being constrained by the need to ensure a feasible structure in terms of physical and spatial bounds. Creating these procedurally is thus a challenge. In this work, we explore multiple approaches to rollercoaster track generation through the use of Markov chains and various deep learning methods. We show that we can achieve relatively good tracks in terms of the game's measurement of success, and that reinforcement learning allows for more control of the generated tracks and for different rider experiences. A focus on multiple measures allows our work to extend to other track properties drawn from real-world research. This paper extends a previous publication by adding a new reward function for our reinforcement learning agent as well as further analyses of the generated tracks, including a metric measuring rider excitement over time, a revised novelty metric and an analysis of controllability.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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