Bibliographic record
Abstract
Key FeaturesDesign and develop game elements that can be used alone or stacked to enhance your gaming experienceExplore extensions that can unlock GameMaker: Studio's true potential to create complex gamesA recipe-based, easy-to-follow guide to take your GameMaker skills to the next levelBook DescriptionGameMaker: Studio started off as a tool capable of creating simple games using a drag-and-drop interface. Since then, it has grown to become a powerful instrument to make release-ready games for PC, Mac, mobile devices, and even current-gen consoles. GameMaker is designed to allow its users to develop games without having to learn any of the complex programming languages such as C++ or Java. It also allows redistribution across multiple platforms.This book teaches you to harness GameMaker: Studio's full potential and take your game development to new heights.It begins by covering the basics and lays a solid foundation for advanced GameMaker concepts. Moving on, it covers topics such as controls, physics, and advanced movement, employing a strategic approach to the learning curve. The book concludes by providing insights into complex concepts such as the GUI, menus, save system, lighting, particles, and VFX.By the end of the book, you will be able to design games using GameMaker: Studio and implement the same techniques in other games you intend to design.What you will learnSet up player control schemes of various types, such as touch controls and a gamepadCreate your own physics sandboxGet accustomed to advanced player movementImplement music and 3D sound in your gamesUtilize GameMaker's GUI layers to create exciting gamesGenerate adjustable game settings and save systemsAdd depth to your game with lighting and special effectsAbout the AuthorBrandon Gardiner is a game developer and designer living out his love of video games. Though he started his foray into games through 3D art and level design, he always kept a notebook of ideas for games and game mechanics he wished to see. When he discovered GameMaker: Studio, he found that he could bring these ideas to life.He is a graduate of the first iteration of the game development program at Toronto's George Brown College. In college, he worked as an artist and designer on several game projects for outside companies, including tie-ins for children's television shows and educational titles. After graduating, he founded MechaBee Studios where, being the sole developer of mobile and PC games, he is a jack of all trades.He also writes a blog at http://www.gamemakerhq.com, through which he hopes to build a resource for other independent developers. He lives with his wife in Toronto where he is a veteran of the annual Toronto Game Jam (tojam.ca), an active member of the International Game Developers Association.Table of ContentsGame Plan Creating Basic GameplayIt's Under Control Exploring Various Control SchemesLet's Move It Advanced Movement and LayoutLet's Get Physical Using GameMaker's Physics SystemNow Hear This! Music and Sound EffectsIt's All GUI! - Creating Graphical User Interface and MenusSaving the Day Saving Game DataLight 'em up! Enhancing Your Game with Lighting TechniquesParticle Man, Particle Man Adding Polish to Your Game with Visual Effects and ParticlesHello, World Creating New Dimensions of Play Through Networking
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.715 | 0.597 |
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".