Fold–thrust structures – where have all the buckles gone?
Bibliographic record
Abstract
Abstract The margins to evolving orogenic belts experience near layer-parallel contraction that can evolve into fold–thrust belts. Developing cross-section-scale understanding of these systems necessitates structural interpretation. However, over the past several decades a false distinction has arisen between some forms of so-called fault-related folding and buckle folding. We investigate the origins of this confusion and seek to develop unified approaches for interpreting fold–thrust belts that incorporate deformation arising both from the amplification of buckling instabilities and from localized shear failures (thrust faults). Discussions are illustrated using short case studies from the Bolivian Subandean chain (Incahuasi anticline), the Canadian Cordillera (Livingstone anticlinorium) and Subalpine chains of France and Switzerland. Only fault–bend folding is purely fault-related and other forms, such as fault-propagation and detachment folds, all involve components of buckling. Better integration of understanding of buckling processes, the geometries and structural evolutions that they generate may help to understand how deformation is distributed within fold–thrust belts. It may also reduce the current biases engendered by adopting a narrow range of idealized geometries when constructing cross-sections and evaluating structural evolution in these systems.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".