Review of: "COVID-19 or Russia-Ukraine conflict: which is informative in defining the dynamic relationship between Bitcoin and major energy commodities?"
Bibliographic record
Abstract
which is informative in defining the dynamic relationship between Bitcoin and major energy commodities?'The paper considers evaluating the returns and volatilities of energy commodity indices and Bitcoin through dynamic conditional correlation analyses [GARCH-DCC (1,1)].It reveals a significant and considerable dynamic conditional correlation between energy commodities indices and Bitcoin when the COVID-19 pandemic and Russia-Ukraine conflict shocks are incorporated in variance assessments.1-There exist two sections of the Introduction.The authors need to revise the whole paper.2-The introduction section (pp: 2-4) needs some citations.For instance; How do know that In Canada and the United States, stock markets quickly lost more than 30%, erasing most of the gains made in recent years?(See 2 nd paragraph of Intro).Or, where can we find the info if, on 24 February alone, the price of natural gas rose by more than 25% on the TTF market, a platform located in the Netherlands and considered a benchmark in Europe?(See p. 2).Or, where a potential reader can confirm the below lines regarding the major consequences of inflation?(See p. 3 ).'This acceleration in inflation could have two major consequences: First, it should weigh on purchasing power, i.e., the ability of households to buy goods and services with their income.If inflation is higher than the increase in household income, then purchasing power declines.This is also the scenario forecast by the National Institute of Statistics and Economic Studies (INSEE) for the year 2022.According to forecasts by the Institute published last December, the
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".