The Australian Experience With Resources, Infrastructure Corridors and Supply Chains
Bibliographic record
Abstract
The North of Canada and the North of Australia are both resource-rich, but have underdeveloped infrastructure, small, scattered populations and high proportions of inhabitants who are Indigenous. The experiences of developing Australia’s North hold lessons for Canada. Experience from development of iron ore mining and gas production in the Pilbara region of Western Australia, and with coal and gas development in the Central Queensland coalfields region, can be applied usefully to development of resources and infrastructure in Canada’s North, as well as in other resource-rich regions of the world. Supply chains that provide efficient transport, handling and processing of mineral and energy products in the two Australian regions, and goods and services inputs to production, have been critical to their success in becoming world-leading resources exporters. Governments and industries in the Pilbara and Central Queensland have followed contrasting pathways to developing infrastructure and supply chains. Each initial pathway had advantages and disadvantages. Approaches by governments and project operators have changed over the 60 years of resources development, in light of experience, in response to big increases in scale of production, to the need to increase efficiency, experience of existing approaches and to accommodate new industries and entrants. Governments have key roles to play in resources regions through their control of land allocation for development, and environmental and social protection. Governments also have responsibilities and unique abilities for co-ordination of development. While discussion in this paper focuses primarily on supply chains for transport of outputs and inputs, another important consideration is infrastructure for people, without which resources projects cannot be developed and operated.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".