Bibliographic record
Abstract
Non-fungible tokens (NFTs) may initially sound like a fancy jargon, but they are actually rather simple to comprehend. Blockchain-based non-fungible tokens (NFTs) are digital items that are often linked to one-of-a-kind digital material, like music or photographs. A multi-billion dollar NFT market has appeared overnight thanks to a recent flurry of public interest. NFT collections are being produced by brands like Coca-Cola, Nike, and conventional artists like Damien Hirst and Grimes, as well as mainstream consumer goods businesses. Individual NFTs may fetch millions or tens of millions of dollars [1]. Introduction It was the ideal setting for a match that has been lit ever since: Sotheby's had to keep up with Christie's pace and organized a fully curated sale with Canadian artist Mad Dog Jones; Open Sea (the leading NFT marketplace across all chains) reported last month a billion-dollar trading volume week; and in the final week of August, it set a record trading volume day of $208 million [2]. The daily volume traded at Open Sea at the time of writing is greater than what the platforms reported for the full year of 2020. Machine learning is frequently used by financial
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.464 | 0.396 |
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".