Modified spatial network approach to predict mobilisation emissions from upstream mineral exploration: applications to British Columbia’s critical mineral sector
Bibliographic record
Abstract
• First methodology for predicting greenhouse gas emissions from mineral exploration. • Upstream mineral exploration industry classified into six key work activities. • Example greenhouse gas inventory as well as data products for decision-making. Reaching global climate targets and the energy transition will require new sources of critical minerals. Exploration and mining activities generate greenhouse gas (GHG) emissions, but the relative contribution of emissions from all portions of the mining value chain remains poorly understood. Here, we divide the upstream portion of the mining value chain (i.e., mineral exploration) into six activities, representative of most Scope 1 emissions. We present spatial methods to estimate GHG emissions for ground and air transport and apply these methods to constrain emissions of three of the six exploration activities. A ground-transport model is informed by a spatial network analysis finding least-cost paths and fuel economy; whereas the air-transport model is informed by straight-line proximity to start locations and aircraft burn rate. We use these models in two applications. First, producing continuous maps of a single exploration activity with an example of field work mobilisation in British Columbia, Canada (i.e., mobilising a single vehicle) that can be used in a comparative fashion, where emissions of the activity can be compared between any two points in the modelling area. Second, a detailed emissions estimation contrasting two hypothetical drill programs, analogous to a GHG inventory when fuel use is not detailed in exploration reports. The methods and example data products presented here demonstrate how large-scale emissions inventories could be completed for the upstream exploration sector and used to define exploration areas prospective for critical minerals that will generate the lowest amount of GHG emissions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".