Bibliographic record
Abstract
causality analysis, 116 Augmented Dickey-Fuller technique (ADF technique), 31-32, 121 Bitcoin (BTC), 154-155, 165-168 and energy efficiency, 167-169 mining, 166-167 nuclear energy efficiency, 169-171 in nuclear power plants, 171-172 Bitcoin US Dollar (BTC/USD), 156 Blockchain technology, 167 Brazil, Russia, India, China, South Africa, and Turkey countries (BRICS-T countries), 29, 116 Brazil, Russia, India, China and South Africa nations (BRICS nations), 28 BRICS-T countries, 29 data and methodology, 31-33 energy use, 33 GCC, 30 GDP, energy use, and carbon emission, 31 individual unit roots test, 34 Johansen cointegration test, 35 stationary test results, 34 VECM analysis, 34, 36 Wald test results, 37 Brent Petroleum, 157 Brent Petroleum Spot US Dollar (XBR/USD), 156 CADF test, 119 Canadian economy, 128 Carbon emission, 2 Carbon footprint, 28 CD LM test, 121 CO 2 emission (CO), 28, 31-32, 114, 168 CoinMarketCap, 154 Commercial growth, 114 Correlation matrix, 157-158 Covid-19 pandemic, 154 Crypto assets, 154-156 Cryptocurrency, 167-168 CUP-BC estimator, 120-121 CUP-FM estimator, 120-121 Current account deficit, 142-143 Decision making trial and evaluation laboratory (DEMATEL), 146 Delta test (D test), 119 Democratic Republic of Congo (DRC), 115 Descriptive statistics, 157-158 Developed economies, 28, 101 Direct employment, 142-143 Disappearing effect, 76-77 Diversification, 157-159 Double flash power plants, 4-5 Ecological Carbon Footprint, 114-115 Ecological Footprint, 114-115 Econometric analysis, 76 Economic growth, 14, 114, 142
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.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.732 | 0.760 |
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".