Modeling and forecasting complex patterns of mineral production. Gold mining in Canada
Bibliographic record
Abstract
The complex pattern of gold production in Canada, which differs from the classic Hubbert´s curve, is modeled, and a short-term forecast is made, via the Fundamental Equation of Mineral Production (FEMP).For intervals of time with a variable production to reserves ratio, a discrete piecewise version of the equation updates the reserves and the Production to Reserves Ratio (PRR) by units of time. These piecewise linear portions of the PRR correspond to sub-cycles of rising and declining production embedded in the overall history of production.This version of the FEMP allows modelling of any pattern of mineral production at a universal range of scales.The Hubbert´s linearization indicates the official account of reserves underestimates the real amount of gold resources in Canada. The equivalence between the Hubbert’s formula and the FEMP is introduced. The Hubbert curve is shown as a case study of the Fundamental Equation, featured as the product of a quadratic function of the cumulative production and a rational function of the PRR with time. The forecast for Canada’s current gold production illustrates the metal output is about to peak with an all-time record high in the near future, unless new findings or influential global economic factors are introduced (i.e., a sharp and sustained growth of gold demand from regional markets), allowing an increase of the slope of the linear function featured by the PRR of the FEMP-based model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".