Editorial: Investigation, monitoring, stability, and risk assessment of geohazards
Bibliographic record
Abstract
of geohazards aims to comprehensively assess the occurrence mechanisms, impact scope, and potential hazards of geohazards through interdisciplinary methods. By developing experimental, analytical, and numerical techniques for characterizing and modeling the evolution mechanisms of geohazards, it is possible to predict and mitigate risks more accurately. The use of advanced data collection, experimental testing, numerical simulations, and monitoring methos offers scientific support for public safety and the reduction of economic losses. In recent years, there has been growing recognition of the importance of enhancing the basic theory of geohazards.To showcase the latest achievements in this area, Research Topic entitled Investigation, monitoring, stability, and risk assessment of geohazards was organized. This research topic includes 14 papers, addressing the sensitivity and risk assessment of geohazards such as earthquakes and landslides, the mechanism and dynamics of debris flows, early warning for debris flows, landslide failure mechanisms, railway tunnel stress state assessment, geohazard identification methods, physical-mechanical properties of rock mass, etc. The findings presented in this research topic provide valuable references for addressing potential geohazards in engineering projects such as hydropower, highways, railways, and open-pit mining.The physical-mechanical properties of geotechnical materials are critical controlling factors in the evolution of geohazards, directly influencing the occurrence, development, and severity of their impacts. These properties include, but are not limited to, the strength, deformation characteristics, permeability, and the development of fractures within the geotechnical mass. Together, they determine the response behavior of geological materials under natural or anthropogenic influences. Therefore, a comprehensive study of the physical-mechanical properties of geotechnical materials is essential for accurately assessing geohazard risks and formulating effective prevention and mitigation strategies. Collectively, these studies offer valuable methodologies for monitoring, assessing, and mitigating risks posed by geohazards, with a focus on the identification of potential hazards, understanding the mechanisms and dynamics of geohazard events, and evaluating risk through advanced methodologies such as numerical simulations, UAVbased imaging, InSAR technology, and clustering techniques.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".