Trend 1980 - 2014. Energy Information Administration. International Energy Statistics: Coal | Country: Canada | Category: CO2 Emissions | Series: CO2 Emissions from Coal | Units: Metric Tons, 1980-2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 004-015-002.
Bibliographic record
Abstract
Energy Information Administration (2017). International Energy Statistics: Coal | Country: Canada | Category: CO2 Emissions | Series: CO2 Emissions from Coal | Units: Metric Tons, 1980-2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 004-015-002. Dataset: Reports statistics related to consumption, production, trade, and more, of coal 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: International Trade, Coal, Energy Consumption, Coal Reserves, Imports, Exports, 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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.030 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.054 |
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".