Cryogenic survival: Analysis and development of 'lost with frost' a 2D Python-based RPG game
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".