Bibliographic record
Abstract
NFTs are non-fungible, one-of-a-kind digital assets that are enabled by blockchain technology. Digital encrypted assets known as non-fungible tokens are one-of-a-kind, rare, and impossible to duplicate. A greater variety of use cases, including as digital art, domain names, gaming, collectibles, and others, have been observed recently for NFTs. On a blockchain, like Ethereum, NFTs are created (i.e., minted), and they can be used to confirm ownership of an asset (where it came from, who is the owner, etc.). Data from a joint analysis by Nonfungible.com and L' Atelier BNP Paribas indicates that 2020 In 2018, the overall market value of the NFT market was around $ 338,035,012 with an annual growth rate of 299%. This excludes wash trading and abandoned projects. Some NFTs cost millions of dollars, which is quite expensive. How can the value of NFTs be fairly honestly evaluated is a common question. Let's analyze the history of NFT's evolution before responding to this query.
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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".