The Grenville Orogen explained? Applications and limitations of integrating numerical models with geological and geophysical dataThis article is one of a series of papers published in this Special Issue on the theme <i>Lithoprobe — parameters, processes, and the evolution of a continent</i>.
Bibliographic record
Abstract
Numerical models offer powerful insights into tectonic processes, especially when their validity can be tested against geological and geophysical observations from natural orogenic belts. Here we explain some of the criteria for success in integrating orogenic models with data, using examples from the Grenville Orogen. Model designs must be simplified by comparison with nature to illuminate the first-order processes that control orogenic evolution, which limits the extent to which model results can reproduce geological observations. For the western Grenville Orogen, observed variations in geological properties are represented by lower crustal blocks with strength decreasing from the exterior to the interior of the model. GO-series models with this design reproduce the first-order crustal architecture of the Georgian Bay and Montréal – Val d’Or Lithoprobe transects. Both constant-convergence and stop-convergence models produce similar geometries, but only stop-convergence models produce normal-sense shear zones like those observed. EGO-series models, incorporating an initial weak zone bounded by stronger lower crustal blocks, predict exhumation of high-pressure rocks as observed in the eastern Grenville Orogen, although other aspects of these model results are not as successful. The single most important test of a geodynamic model is its ability to integrate diverse and independent observations in a self-consistent manner. Other criteria include consistency with crustal-scale geometry and structural and metamorphic histories. By these criteria, the present models account reasonably well for the syn- and post-convergent evolution of the western Grenville Orogen, but further work is required to produce a fully satisfactory model for the eastern end of the system.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".