Olympic Medal Count Prediction Model for Various Countries based on LSTM and Supervised Machine Learning
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
The acquisition of Olympic medals holds significant importance for the development of a country's sports endeavors. This paper constructs a medal prediction model based on TOPSIS-LSTM model and supervised learning, utilizing historical Olympic data. The Random Forest algorithm is employed to forecast the medal performance of countries at the 2028 Los Angeles Olympics. The results indicate that the United States will achieve 126 medals, while China will secure 91 medals, ranking first and second, respectively. The United Kingdom and Canada follow closely with 65 and 55 medals, respectively. The model's RMSE is less than 5.8, and the R2 value is greater than 0.93, indicating a relatively good fit.
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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 | 0.001 | 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