(D)rifting in the 21 <sup>st</sup> century: Key processes, natural hazards and geo-resources
Bibliographic record
Abstract
Abstract. Rifting and continental break-up is a key research topic within geosciences, and a thorough understanding of the processes involved, as well as of the associated natural hazard and natural resources is of great importance to both science and society. As a result, a large body of knowledge is available in the literature, yet most of previous research focuses on tectonic and geodynamic processes and their links to the evolution of rift systems. However, we believe that the key challenge for researchers is to make our knowledge of rift systems available and applicable to face new societal challenges. In particular, we should embrace a system analysis approach, and aim to apply our knowledge to better understand the links between rift processes, natural hazards, and the geo-resources that are of critical importance to realize the energy transition and a sustainable future. The aim of this paper is therefore to provide a first-order framework for such an approach, by providing an up-to-date summary of rifting processes, hazards, and geo-resources, followed by an assessment of future challenges and opportunities for research. We address the varied terminology used to characterise rifting in the scientific literature, followed by a description of rifting processes with a focus on the impact of (1) rheology and stain rates, (2) inheritance in three dimensions, (3) magmatism, and (4) surface processes. Subsequently, we address the considerable natural hazards and risks that occur in rift settings, which are linked to (I) seismicity, (II) magmatism, and (III) mass wasting, and provide some insights in how the impacts of these hazards can be mitigated. Moreover, we classify and describe the geo-resources occurring in rift environments as (a) non-energy resources, (b) geo-energy resources, (c) water and soils, and (d) opportunities for geological storage. Finally, we discuss the key challenges for the future linked to the aforementioned themes, and identify numerous opportunities for follow-up research and knowledge application. In particular, we see great potential in systematic knowledge transfer and collaboration between researchers, industry partners and government bodies, which may be the key to future successes and advancements.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".