Are Ore Grades Declining? The Canadian Experience, 1939-89
Bibliographic record
Abstract
Economists claim that it pays to mine high-grade mineral deposits first and lower-grade deposits later. Moreover, it is often assumed that relatively few ore bodies remain to be found that would be of higher quality than those already discovered. According to this line of reasoning, in the course of a nation’s mining history the average grades of ore mined will decline as the higher-grade deposits are gradually depleted. The example most commonly cited as evidence of this sequence of events is the drop in the average grade of copper ore mined in the United States-from more than 3.0 percent around 1900 to about 0.5 percent today. It is often inferred from such evidence that countries with a significant history of mining have been forced to exploit lower-grade ores and are at a competitive disadvantage vis-a-vis countries with a shorter history of mining.The logic of this theory-that resource depletion inevitably leads to exploitation of lower-grade resources-seems unassailable, but the length of time and the rate of exploitation necessary to bring about such change remain unclear. In Canada, the dramatic growth in mining during the past quarter-century has led some people to wonder whether resource depletion might not already have pushed that country past the peak of its golden age of mining. Challenging the idea that Canada may be running out of good ores at this stage in its mining history, this chapter assesses that concern by examining the evidence with regard to the decline of ore grades in Canadian mining for seven important metals: copper, zinc, lead, nickel, molybdenum, silver, and gold.The next section explains how the assessment is carried out, after which trends in the number of operating mines and their output since 419 MARTIN AND JEN 1939 are examined. A mctal-by-metal analysis of grade changes since 1939 is then given, and the final section presents the conclusions of the chapter.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.029 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".