| Token | #7576 of SciMatic Articles (ERC-1155), fixed supply 1,000,000 |
|---|---|
| Contract | 0x0E596B4bb924937aA35D9b342A90347ed6F05eF8 |
| Mint transaction | 0xfa308196c68af2f829d36bed84ec6f748ec39382b359a4162c299d25aae6f294 · block 12110437 |
| Minted | 2026-10-02 12:05 UTC |
| Hash on chain | 0x8d0b693741ea27202043d9813265d33ce2f4655446e180c1ef3394cbde69190e |
| SHA-256 of the record | 0x8d0b693741ea27202043d9813265d33ce2f4655446e180c1ef3394cbde69190e |
| Article today | Matches the anchored record. |
| Holder | Wallet | Minted | Holds now |
|---|---|---|---|
| SciMatic (10%) | 0xc35709AB…277732 | 100,000 | 100,000 |
| Said Nadeem | 0x75286a68…bEDc8A | 900,000 | 900,000 |
Canonical JSON (keys sorted, no spaces, UTF-8). Its SHA-256 is the hash on chain. The abstract here is gemini generated, not the author's own text.
{"abstract":"This study proposes an AI-supported framework to predict student academic performance using behavioral and engagement data collected from digital learning platforms. The research aims to address the limitations of traditional evaluation methods by leveraging machine learning algorithms to analyze student interaction data, including LMS usage, assignment submissions, and attendance. The approach involves collecting and transforming this data into meaningful features, then applying various machine learning models such as Random Forest, Support Vector Machine, and Gradient Boosting. These models are evaluated using metrics like accuracy, precision, recall, and F1-score. The expected outcome is the early identification of at-risk students, enabling data-driven academic support strategies and improved overall student success rates.","abstract_source":"gemini_generated","authors":["Said Nadeem"],"date":"2026-04-12","doi":null,"id":7576,"issue":3,"journal":{"issn":"3150-0390","name":"Journal of Unique and Crazy Ideas"},"pdf_sha256":"c8f8ded7d18d96e7ce5abc17696cb8e6c983afbe6380e8f0d9165b72813be8bc","schema":"scimatic-article/1","title":"AI-Supported Academic Performance Prediction Using Behavioral and Engagement Data","volume":1}
1. The record, from the token's own metadata:
printf '%s' "$(curl -s https://scimatic.org/nft/articles/7576.json | jq -r .canonical_record)" | sha256sum
2. The hash stored on chain (contentHash(7576)):
curl -s https://rpc.scimatic.net -H 'content-type: application/json' \
-d '{"jsonrpc":"2.0","id":1,"method":"eth_call","params":[{"to":"0x0E596B4bb924937aA35D9b342A90347ed6F05eF8","data":"0xf3e0c2900000000000000000000000000000000000000000000000000000000000001d98"},"latest"]}'
The two must be equal (the second with a 0x in front).