A Discussion on Geodynamic Modeling Methodology: Inferences from Numerical Models in the Anatolian Plate
Bibliographic record
Abstract
Numerical models have found widespread use in geosciences, mainly due to high-resolution datasets and the development of supercomputing facilities with powerful data processing and storage capabilities during the past two decades. Instantaneous and time-dependent geodynamic modeling studies were carried out in many regions of the Alpine-Himalayan orogenic belt, including the Anatolian Plate, to investigate mantle dynamics such as lower lithosphere deformation, upper mantle flow, and their surface implications. This study focuses on the instantaneous numerical modeling technique by considering multidimensional thermomechanical models in the Central and East Anatolian plateaus. To this end, conventional geodynamic modeling processes are explained with a conceptual flow chart that shows a feed-forward backpropagation modeling architecture which is nonlinearly fed by a large parameter space. While addressing a complex natural phenomenon controlled by variables on a wide range of space-time scales, the limitations as well as advantages of numerical models are analyzed. In addition to conventional techniques, systematic data improvement is discussed as a new strategy in data/parameter-dependent numerical model design through an iterative process based on the Grounded Theory method for the construction of an explanatory theory from the model. This involves not refinement but (re)construction of the data (i.e., measurement/analysis/scaling) as an effective way to reveal theory/information grounded in data. It is speculated that this procedure, which includes problem-oriented data reconstruction accompanying the numerical modeling process, may provide an integrated perspective for instantaneous numerical modelling.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".