Thermochemical structure of Canada using multi-observable probabilistic inversion
Bibliographic record
Abstract
The continental lithosphere of Canada, spanning over 3000 kilometres between the Atlantic, Pacific, and Arctic Oceans, exhibits a complex tectonic history characterized by Archean cratonic blocks, Proterozoic collisional orogens, and distinctive shear zones. This study focuses on constructing comprehensive thermochemical models of the Canadian lithosphere, employing a suite of modelling codes (LitMod) that integrate multiple geophysical observables, and geochemical and mineral physical data within a rigorous thermodynamic framework. The lithospheric structure and processes are examined through a two-part inversion approach, with the first part utilizing seismic datasets (Rayleigh wave dispersion and body wave data), global reference models, elevation, geoid, and surface heat flow measurements. The study aims to overcome the limitations of single-data-driven approaches, providing holistic insights into the structural changes in the crust and upper mantle. The results are expected to advance our understanding of the lithosphere’s influence on surface processes, mineral prospectivity, Glacio-Isostatic Adjustment modelling, Carbon Capture, Utilization, and Storage (CCUS), and seismic hazards, among other applications. The report outlines the methodology, data inputs, and the ongoing 1-D testing phase, emphasizing the importance of validating outputs against independent studies and xenolith data to ensure robustness. The ultimate goal is to contribute to a comprehensive physical model of the Canadian lithosphere with broader implications for Earth sciences and various industries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".