The geography of energy consumption of the Canadian metals processing industry
Bibliographic record
Abstract
This study was initiated (a) to determine the types and quantities of energy consumed by the Canadian metal processing industry on a plant basis, (b) to examine the manner and extent to which the energy consumed varies spatially across Canada and (c) to determine the extent to which the industry contributes to the nation's total energy consumption as well as to its consumption of each energy source. The energy consumption mixes of individual plants were determined either empirically or by estimation from data obtained by a questionnaire and from the technical literature for the comminution, smelting, and refining stages of the lead, zinc, nickel, copper, aluminum, and primary iron and steel industries. The spatial variation of the consumption mixes for each metallurgical process was then delimited and illustrated cartographically. The specific and non-specific energy needs of each industry are identified in terms of the processes used at each production stage. It was found that the types and quantities of energy utilized by each plant depends upon the process’ specific energy needs and the availability of other energy forms. The metal processing industry was found to consume a significant proportion (8.2%) of Canada's energy consumption in 1965. In terms of individual sources of energy the industry consumed 23.4% of the national total consumption of electricity, 14.6% of coal and less than 3% of petroleum and natural gas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".