Bibliographic record
Abstract
Co-Space is a co-existence of real world in a virtual environment where it reflects the physical world in terms of content, facilities and structures. Nanyang Technological University (NTU) has developed its very own Co-Space to empower users with the benefit to explore and understand NTU better in the comfort of their seats. This project aims to improve and further develop the existing NTU Co-Space by adding new scenes. By modeling and implementing the fast food stalls located in NTU a new scene named Makan Place is created to hold these 3D models. Interactive contents will then be added to make exploration more realistic and interesting. \n \nThis project is broken into 5 phases, Research and Analyze, Modeling, Cashier NPC Design, Knowledge Implementation and Integration. Initially, research and analysis was conducted to decide how the stalls are to be modeled. The stalls to be modeled are McDonald’s, SubWay, Canadian Pizza, and Old Chang Kee. In the Modeling phase, these stalls were modeled into 3D using Autodesk 3ds Max 2010. A Cashier Non-Player Character (NPC) was designed and created and it will be placed at each stall. These Casher NPCs will be representing each fast food stall and they are implanted with some knowledge. This is done using Artificial Intelligence Mark-Up Language (AIML). All these will be integrated into the existing Co-Space using Unity 3D. Interactive contents that were also developed include playing a video and pop-up menu. \n \nThe long term plan of Co-Spaces is to mimic the real world as closely as possible. Hence, there will always be room for improvements even with the completion of this project. Improvements that can be made are purchasing food using credits and animations that the user’s player is eating food can be made possible in the future developments of NTU Co-Space. NPCs of NTU Co-Space could also be added to wander round the Makan Place. This will create a scene that the canteen is a buzzing place to be. These recommendations will help make NTU Co-Space more informative for users and aid them in experiencing the vibrant life in NTU.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".