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Comment on egusphere-2023-2548

2024· peer-review· en· W4390584285 on OpenAlexaff

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRiftTerminologyNatural resourceEarth scienceNatural (archaeology)East African RiftGeologyMagmatismHazardTectonicsPaleontologyPolitical scienceEcology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.300
Threshold uncertainty score0.999

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0290.014
Insufficient payload (model declined to judge)0.3000.218

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.

Opus teacher head0.040
GPT teacher head0.275
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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