Bibliographic record
Abstract
This chapter describes how structural and stratigraphic architectures involving reservoirs combined with seals represent hydrocarbon traps and control their structural, stratigraphic, or combined character in strike-slip and transform margin settings. It talks about their characteristics. Structural traps evolve with their controlling strike-slip faults that develop as not steady-state features in the continental lithosphere. The trap geometry develops in response to controlling mechanical stratigraphy and local stress field undergoing constant changes. Different structural traps in the same mature strike-slip fault zone may have been developed in different stages of its development. Older ones may have been modified during the younger stages of the strike-slip fault or subsequent event. Some structural traps can be associated with the strike-slip fault itself, others with its horse-tail structures, some with the region between the two interacting strike-slip faults, others with the tectonic setting hosting the strike-slip fault, modified by the interaction of the hosting setting with developing strike-slip fault. The environment where the strike-slip fault develops may have its own suite of pre-existing traps that get modified by the strike-slip-related deformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.235 | 0.077 |
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".