Modeling and forecasting complex patterns of mineral production. Gold mining in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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) | 0.000 | 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 it