Bibliographic record
Abstract
This report uses machine learning models to analyze Olympic medal data and uncover hidden insights. We applied KNN to handle missing data and Box-plot to remove outliers. We then built a Mixed Effects Model to identify factors influencing medal counts and used XGBoost to predict the 2028 Olympic medal table, achieving MSEs of 1.0166 (gold), 4.3638 (silver), and 7.8201 (bronze). Additionally, we quantified resource investment using competition and athlete numbers, predicted future trends with ARIMA, and used logistic regression to identify potential first-time medalists like Luxembourg, Angola, and Guinea. We employed the DID method to evaluate the impact of excellent coaches on medal outcomes and used Random Forest to predict the benefits of investing in coaches for specific events, such as France's Basketball, Canada's Water Polo, and the UK's Hockey. We categorized events using HHI and Shannon Diversity Index, finding that evenly distributed events attract more participating countries, promoting sport development. Finally, sensitivity analysis confirmed the XGBoost model's robustness, though some parameter definitions require refinement for improved accuracy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".