Fold-thrust structures: conceptual understanding, anchors and uncertainties
Bibliographic record
Abstract
Concepts are a critical element in our ability to understand and categorise natural phenomena. Indeed, the term\nfold-thrust belts is used to categorise the different natural phenomena that together define the set of structures\ncommonly found in compressional tectonic settings.Within this over-arching term sub-terms are used to categorise\nthe geometries and evolution of different sub-sets of structures. These categorizations (e.g. fault-bend fold, fault\npropagation fold etc.) include inherent conceptualisations of fold-thrust structure evolution. The concepts are\ndominated by geometrical constraints and for thrusting, by its nature, deformation localisation. We argue that the\ntrue interplay of the deformation characteristics and the controls that underpin folding and thrusting are not fully\nintegrated or represented in these categorisations. Yet we are apparently anchored to these conceptual models of\nfold-thrust belts and their structures. Evidence suggests that anchoring occurs early; from teaching examples based\non simple conceptual forms, that then dominate individuals’ future conceptual models of structures and how they\nevolve. How we present and use models, at an early stage, to explain and represent concepts can have significant\naffects.\nHere we present a series of case studies of fold-thrust belts that we use to observe the structural geometries\nand consider the different controlling mechanisms at play in their evolution. Our case studies span seismic\ninterpretations of deep-water fold-thrust structures including the Niger delta and Sabah, and field outcrops at a\nrange of scales from the Canadian Rockies to the French subalpine chain and the Variscan of S. Wales. We use\nthese case-studies to reflect on the applicability of existing conceptual models to classify fold-thrust structures. In\ndoing so we question whether the dominant existing concepts allow useful categorisation of fold-thrust structures\nto address questions such as: uncertainties in fault location, connectivity of fold-forelimb stratigraphy and the\nuse of evolutionary models to predict fault displacements and fracture patterns. We propose that a greater range\nof conceptual models are required. New concepts that better represent the observed natural phenomena should\nimprove understanding of uncertainties in fold-thrust belt models and inform probabilities of elements such as\nfault placement and linkage to help address outstanding questions in fold-thrust belt research.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.013 | 0.039 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".