Evaluating the risk perception due to land subsidence within onyeama mine, South East Nigeria
Bibliographic record
Abstract
This paper conducts a systematic evaluation of risk perception due to land subsidence monitored within a major coalmine in Nigeria. There is a general assumption in most hazard research that risk perception should be a first determinant of whether adaptive action is taken or not. To gain a comprehensive insight using land subsidence, we propose a technique that integrates quantitative evaluation (vulnerability assessment) and subjective evaluation (perception analysis) of human responses towards this environmental hazard. Perception of risk, which is dependent on risk magnitude appraisal and risk acceptance, was amplified into the overall risk constellation. Vulnerability assessment was conducted based on the human development index (HDI) at fourteen locations investigated for the study, with particular focus on building networks as an index for loss estimation. We exploit data from Sentinel-1 Synthetic Aperture Radar (SAR) Satellites and Small-Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) technique to map Onyeama Coal Mine in South – East, Nigeria. From the HDI and according to the vulnerability assessment, the margin of error (E) is estimated as 0.06754, with samples proportion of ρ ˆ = 0.85. The 95 % confidence interval for proportion is in the range of 0.783 to 0.917. The results indicates that with an average of a finite population size of N = 1413 buildings at each study location, about 85 % of the samples ( n = 100 ) do not recognize that land subsidence is ongoing and constantly affecting their buildings. As a result of this ignorance and lack of awareness, constantly progressing subsidence becomes normalized in peoples’ perceptions, and their outlook toward danger is not integrated into day-to-day habits. Thus, risk perception is a lesser determinant of mitigation responses towards slowly progressing subsidence, and not actual exposure leading to action. • From the HDI and according to the vulnerability assessment, the margin of error (E) is estimated as 0.06754, with samples proportion of ρ ˆ = 0.85. • The 95 % confidence interval for proportion is in the range of 0.783 to 0.917. • The results indicates that with an average of a finite population size of N = 1413 buildings at each study location, about 85 % of the samples ( n = 100 ) do not recognize that land subsidence is ongoing and constantly affecting their buildings. • As a result of this ignorance and lack of awareness, constantly progressing subsidence becomes normalized in peoples’ perceptions, and their outlook toward danger is not integrated into day-to-day habits. • Thus, risk perception is a lesser determinant of mitigation responses towards slowly progressing subsidence, and not actual exposure leading to action.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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; both teacher heads agree on what is shown here.
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".