Advantages and Practical Dilemmas of the Application of Blockchain Depository Technology in Digital Music Infringement Cases
Bibliographic record
Abstract
With the continuous improvement of blockchain depository technology, its application advantages in digital music infringement cases in China are becoming more and more prominent. Blockchain depository technology in digital music infringement cases has the application advantages of effectively proving the focus facts, reducing the cost of proof for plaintiffs, and improving the efficiency of court trials. Nonetheless, blockchain deposit technology still exists in specific practice, such as the problem of data authenticity before entering the chain, the problem of judging the originality of digital music, and the problem of judges' professional level being difficult to adapt to. Based on this, the optimisation path of blockchain deposit technology in digital music infringement cases can be fully utilised by compressing the entry time, increasing the number of times of entry, exploring the mode of "blockchain technology + expert appraisal", and perfecting the relevant rules of evidence.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".