‘Metals production and the environment' + 50 a U.S.A.-centered case study
Bibliographic record
Abstract
In the early 1970's, the U.S.A. was riven by social and environmental forces. Environmental degradation, attributed to irresponsible manufacturing and rapid economic growth, appeared rampant. Energy utilisation and conservation became important. Economists predicted a shortage of metals. Methods to slow growth were proposed. In his new course ‘Metals Production and the Environment' H.H. Kellogg reviewed energy and environmental factors in U.S.A. production of steel, Cu, Zn, Pb and Al. Comparison with the early 21st century illuminates the subsequent transformation. Steelmaking BOF's replaced open hearth furnaces. Continuous casting and secondary refining produced higher quality steels. Direct reduction was commercialised. Copper flash and bath smelting furnaces provided efficiency, pollution abatement and energy conservation. Larger equipment treated lower grade ores. Zinc pyrometallurgy ceased because of economic, environmental and workplace issues. Pb use in gasoline and paint was banned promoting human health. 1970 aluminium production was characterised by high energy consumption, fluoride emissions and low recycle rates. Each parameter has improved. Now, the challenge is to decarbonise processes and simultaneously extract/refine critical minerals. To reach ‘net zero' full effort is required including economic adjustments, e.g. directing mineral and energy resources to crucial activities and moderating consumption.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".