Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Making a team with optimal synergy is essential for competitive success in strategy games like Pokémon. This study attempts an XGBoost-based recommendation model that forecasts synergistic Pokémon pairings utilizing numerical base stats and symbolic domain-specific features, incorporating type synergy scores and one-hot encodings of primary types. The paper applies 1,000 graded Gen9OU matches from Pokémon Showdown to produce a dataset as a binary classification task. The 49-dimensional advanced model, which has an Area Under the Curve of 0.824 and a precision of 0.766, shows the significance of type-based features in emulating team synergy. It provides notable performance improvements from the 12-dimensional baseline model. The planned strategy is compatible with the fundamental principles of Pokémon battles, which significantly affect outcomes. In order to enhance the accuracy and practical application, future work will likely investigate richer battle features, multi-core combinations, and larger datasets. Overall, this research underscores the potential of machine learning to systematically capture and predict strategic synergy in competitive gaming.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it