Bibliographic record
Abstract
The never-ending changes in the mineral industry environment require fast reactions on the part of governments in adapting their mining tax policies. The fiscal analysis software developed for this Master of Engineering and commissioned by the Quebec Ministry of Natural Resources provides the provincial authorities with a quick method of assessing the tax burden of a mining project located in Quebec. It also allows comparison of Quebec's tax burden with that of other Canadian mining provinces as well as the analysis of fiscal changes on a mine's profitability. The use of the software is illustrated by analyzing the effect of inflation and price cycles on the tax burden of a hypothetical mining project located in Quebec. The behavior of specific tax provisions with respect to these factors is emphasized. The report starts with a general review of mineral resource taxation and fiscal instruments available to governments. This is followed by the documentation of mineral taxation in Quebec, Ontario and British Columbia, three important Canadian mining provinces. The general design and programming of tax analysis software is then described and discussed. The thesis concludes with an analysis of two major economic factors that impact on the tax burden of a mining project, inflation and commodity price cycles.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.033 |
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".