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
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.
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) | 0.004 | 0.001 |
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