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Record W4415906232 · doi:10.1016/j.exis.2025.101779

Modified spatial network approach to predict mobilisation emissions from upstream mineral exploration: applications to British Columbia’s critical mineral sector

2025· article· en· W4415906232 on OpenAlexafffundabout
Daniel S. Coutts, C J M Lawley, Qian Zhang

Bibliographic record

VenueThe Extractive Industries and Society · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsQueen's UniversityCarleton UniversityGeological Survey of Canada
FundersCommission Géologique du CanadaNatural Resources Canada
KeywordsGreenhouse gasUpstream (networking)Work (physics)Energy sectorDownstream (manufacturing)Scope (computer science)Climate changeMineral explorationGlobal warming

Abstract

fetched live from OpenAlex

• 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.304

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.033
GPT teacher head0.256
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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