Trend 1980 - 2015. Energy Information Administration. International Energy Statistics: Electricity | Country: Canada | Category: Generation | Series: Nuclear Electricity Net Generation | Units: Btu, 1980-2015. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 004-015-003.
Bibliographic record
Abstract
Energy Information Administration (2017). International Energy Statistics: Electricity | Country: Canada | Category: Generation | Series: Nuclear Electricity Net Generation | Units: Btu, 1980-2015. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 004-015-003. Dataset: Reports statistics related to consumption, production, and more, of electricity by nation and nation aggregates. The dataset provides data for over 200 countries, as available, on energy-related metrics, including production, consumption, reserves and capacity, imports, and exports, by energy source. Data are sourced from Energy Information Administration research, as well as from national and international agencies, listed at http://www.eia.gov/cfapps/ipdbproject/docs/sources.cfm. Category: International Relations and Trade, Energy Resources and Industries Source: Energy Information Administration The Energy Information Administration (EIA), created by Congress in 1977, is an independent statistical and analytical agency within the United States Department of Energy. Its mission is to provide policy-independent data, forecasts, and analyses to promote sound policy making, efficient markets, and public understanding regarding energy and its interaction with the economy and the environment. http://www.eia.doe.gov/ Subject: Electricity, Energy Consumption, Renewable Energy, Energy Supply, Energy Production
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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 teacher head, 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".