March of the Froblins
Bibliographic record
Abstract
Artificial intelligence (AI) is generally considered to be one of the key components of a computer game. Sometimes when we play a game, we may wish that the computer opponents were written better. At those times while playing against the computer, we feel that the game is unbalanced. Perhaps the computer player has been given different set of rules, or uses the same rules, but has more resources (health, weapons, etc.). The complexity of underlying AI systems, along with game design, belies the resulting feeling we have when playing any game. As the CPU and GPU speed and power continues to grow, along with increasing memory amounts and bandwidth, game developers are constantly improving the graphics of their games. In the last five years the production quality of games has been increasing (along with the corresponding budgets). Recent games woo players with incredible breakthroughs in real- time 3D graphics, complexity of the worlds and characters, as well as various post-processing effects. And while there had been tremendous improvements for parallelizing rendering through the evolution of consumer GPU pipelines, artificial intelligence computations are treading behind. To date, there had been rather few attempts at parallelizing AI computations.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.214 | 0.087 |
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".