Effect of Cryptocurrency Trade on Select Real Currency and Commodity Trading
Bibliographic record
Abstract
Cryptocurrency marked its presence with the inception of bitcoin in the year 2009.Bitcoin was created as a medium for peer to peer exchange.Cryptocurrency can be defined as money in digital form which can be used for exchange of goods and services.The cryptocurrency was designed to discover a type of currency which is not government regulated or in simpler terms currency whose demand and supply cannot be regulated by authorities.Cryptocurrency use cryptography to generate and allocate currency.The process of cryptography requires different verification of transactions without involving any king of centralized authority.To verify that no currency unit is spent twice, transaction verification authenticates the amount of transaction as well as the ownership of the currency.This step by step process is known as mining.The transaction record of cryptocurrency is stored in a ledger called blockchain.Blockchain can be defined as a distributed ledger which forms the basis for cryptocurrency market but its implication is not restricted to only cryptocurrency, blockchain technology can be used in financial services, supply chain management, government documentation.Mining algorithms in most of the cryptocurrency are public.Cryptocurrency presence was not felt only because of its concept but the controversies associated with it like the money laundering and terrorism activities being funded through its mechanism.The cryptocurrency market has shown turbulent with significant growth as well as downfalls.Since the main purpose of cryptocurrency was to replace real currency the question arises whether it has been able to impact the value of real currency in any significant way or not.This research aims at studying the correlation between cryptocurrencies namely Bitcoin, Bitcoin Cash, Neo, Ripple, Ethereum, Tron, Litecoin, Dash, Monero, IOTA and real currencies namely Australian dollar, Canadian dollar, Euro, Pound sterling, Japanese yen, Chinese yuan and commodities namely gold, silver keeping united states dollar as base for all conversions. REVIEW OF LITERATURECryptocurrency is a relatively new concept in the financial market.Although a few researchers and practitioners have researched the area but there is still much research that can be conducted in this field in the near future.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".