Cryogenic survival: Analysis and development of 'lost with frost' a 2D Python-based RPG game
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
This paper presents "Lost with Frost," a 2D Python-based RPG game developed by a team of three. The game immerses the player in a challenging survival experience within a frigid, snowy landscape, requiring strategic resource gathering and enemy encounters. The objective is to sustain the character's health by collecting sticks to fuel a central firepit. The paper details the game's core mechanics, including resource management, survival elements, and enemy interactions. It also highlights the iterative development process, addressing challenges faced and showcasing the team's creative and technical efforts. The game's design concept evolved from brainstorming sessions, emphasizing hardcore survival and exploration in a frozen land. The gameplay is dynamic, with the addition of day-night cycles affecting visibility and immersion. Unique features like dynamic lighting, UI enhancements, and sound control further enrich the gaming experience. The paper delves into the challenges encountered during development, such as refining map generation, object distribution, and gameplay balance. The team's journey through these challenges offers valuable insights into game creation, resulting in an engaging and visually appealing RPG adventure that transports players to a frosty, perilous world.
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 | 0.001 |
| 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