Blockchainizing the Wordle Game in Advanced Metaverse Realms Using Smart Wearables
Bibliographic record
Abstract
6G will enable not only the intelligentization but also the blockchainization of future mobile networks based on decentralized Web 3.0 technologies. A variety of fundamental technologies need to be integrated in 6G in order to drive the implementation of the next Internet, referred to as the Metaverse, through virtual and augmented reality (VR/AR). In addition to incentivizing transactions, the Metaverse should provide gamified experiences, given that gamification will encompass the activity and story around emerging Web 3.0 blockchain technologies. In this paper, we focus on the Wordle game, which was developed during Covid-19 lockdowns and became a worldwide Internet phenomenon. In this paper, we adopt the original Wordle game to the Metaverse, including but not limited to VR and AR. Specifically, we design and experimentally investigate advanced cognitive cues for playing the Wordle game in the eight different experience realms of the so-called Multiverse, the anticipated successor of the Metaverse, using state-of-the-art smart wearables. In addition, we develop a blockchainized version of the game that allows remote experts to play-to-earn tokens and cooperate with local players by providing them with cognitive assistance via smart wearables.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".