Commercial video recognition system for short video (TikTok) based on 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
Short video has the features of short duration and high information carrying capacity, which is more in accordance with modern netizens' mobile phone using patterns. With the continual increase of the user scale of smart mobile terminals, many mobile phone users may make full use of the fragmented time to shoot and view short movies. Numerous Internet behemoths are fighting to invest in creating short video platforms since the amount of video user traffic generates enormous commercial prospects. For speeding up the audit team’s effectiveness, video classification technology needs to be constantly developed and updated. The article proposed a commercial video detection model with a wide range of data analysis and processing. More specifically, Principal Component Analysis (PCA), feature selection by random forest and discretization using decision trees would be involved in order to transform the original data into features that better express the nature of the problem. The application of these features to Random Forest Model can improve the model prediction accuracy of data. Experimental results demonstrate that the recognition system fulfills outstanding performance. The model achieves 0.90 precision and 0.96 AUC score (area under ROC curve) of excellent evaluation in the corresponding test set.
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