Bibliographic record
Abstract
In computer graphics, realistic 3D modeling has been becoming a challenging research area. The terrain synthesis, one of the 3D models, is being developed with two approaches: real geographical data based and fractal terrain. Evolutionary Computation (EC) mimics Darwin's principles of natural evolution processes by representing a chromosome and then applying crossover and mutation. In this thesis, we are interested in how the EC applies to terrain modeling and generates a self-adaptive dynamic terrain system, which can adapt to the personal mannerism of different users. The thesis reviews the background and related works about terrain synthesis and evolutionary computation including related theories and practices. This thesis presents a dynamic adaptive terrain system based on Evolutionary Computation - a specific genetic algorithm. The EC algorithm has been designed and implemented in the EC module; the graphic terrain module shows the result of terrain. The system requires a user's input - mouse click event, drive the interaction between the EC module and the dynamic graphic terrain module. Based on the results that the system has given, the thesis gives out some analysis and conclusions - strengths and weaknesses. The thesis also proposes future works so that researchers picking up this work in future have the benefit of the ideas that thesis generated.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".