Glacially induced stresses and strains in Canada
Bibliographic record
Abstract
Dataset of finite element (FE) model input files for software Abaqus and rebound stress and strain tensor results in 2.5 km depth. FE_models.zip contains Abaqus input files for 7 different FE models of glacial isostatic adjustment (GIA) in Canada. The GIA models use ice model f0050rn001_2022 with 147 time steps, which is a special version of the well known ICE-6G_C GIA-based reconstruction (Argus et al., 2014; Peltier et al., 2015). It combines University of Toronto Glacial Systems Model simulations of the second generation (2.0) with updated ice physics and simulation of surface hydrology (lakes and rivers) (Stuhne & Peltier, 2016, 2017). The loading files also contain load changes from sea level variations corresponding to global ice load changes on the selected Earth model. There are 7 Earth models with different rheologic structure. Details can be found in the corresponding manuscript (Steffen & Steffen, 2024). results.tar.gz contains the rebound stress and strain tensors in 2.5 km depth in Canada for each time step of a GIA model, resulting in 1029 (147*7) files named "{earth_model_name}_stress_strain_{time_in_1000_years_before_present}.dat". The files contain both the internal coordinates of the model and their corresponding longitude and latitude. Note that the grid is partly irregular as there are model parts which have a finer mesh. The format of the files is:x-coordinate of model in km, y-coordinate of model in km, longitude in degree E, latitude in degree N, GIA stress tensor (S11, S22, S33, S12, S13, S23) in MPa and strain tensor (E11, E22, E33, E12, E13, E23) in strain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".