A dataset on metal-related production activities and their socio-environmental impacts in Canada
Bibliographic record
Abstract
Metal-related production activities are essential to the low-carbon energy transition but can generate significant social and environmental impacts that influence project success and public acceptance. The MetalliCan dataset compiles and structures data from 23 open datasets and over 150 reports from more than 40 companies in metal-related sectors, offering a high-resolution, site-specific foundation for sustainability impact analysis in Canada. It was constructed following a systematic and reproducible procedure to integrate heterogeneous data sources at the finest granularity possible and ensure traceability and interoperability. MetalliCan covers 48 commodities and 270 domestic sites including active mines, smelters and refineries, as well as advanced projects. It contains information on environmental dimensions-e.g. greenhouse gases, pollutants, water, land, material use and waste-and social dimensions-e.g. affected population, conflicts, protected lands, future water risk and climate conditions. The code and dataset are openly accessible and can be exploited for industrial ecology research, such as life-cycle assessment, material flow analysis and environmental-extended input-output, as well as criticality, social and prospective studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".