Facilitating the elicitation of beliefs for use in Bayesian Belief modelling

Hassall, Kirsty L.; Dailey, Gordon; Zawadzka, Joanna; Milne, Alice E.; Harris, J. Arthur; Corstanje, Ronald; Whitmore, A. P.

doi:10.1016/j.envsoft.2019.104539

Cited by 21 publications

(29 citation statements)

References 22 publications

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“…The application provides a starting point for defining the overall shape of a conditional probability distribution, which is done by ranking the direction and magnitude of the parent nodes on the child node and populating the table through a scoring algorithm. 68 …”

Section: Methodsmentioning

confidence: 99%

Causal Approach to Determining the Environmental Risks of Seabed Mining

Kaikkonen

Helle

Kostamo

et al. 2021

Environ. Sci. Technol.

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Mineral deposits containing commercially exploitable metals are of interest for seabed mineral extraction in both the deep sea and shallow sea areas. However, the development of seafloor mining is underpinned by high uncertainties on the implementation of the activities and their consequences for the environment. To avoid unbridled expansion of maritime activities, the environmental risks of new types of activities should be carefully evaluated prior to permitting them, yet observational data on the impacts is mostly missing. Here, we examine the environmental risks of seabed mining using a causal, probabilistic network approach. Drawing on a series of expert interviews, we outline the cause-effect pathways related to seabed mining activities to inform quantitative risk assessments. The approach consists of (1) iterative model building with experts to identify the causal connections between seabed mining activities and the affected ecosystem components and (2) quantitative probabilistic modeling. We demonstrate the approach in the Baltic Sea, where seabed mining been has tested and the ecosystem is well studied. The model is used to provide estimates of mortality of benthic fauna under alternative mining scenarios, offering a quantitative means to highlight the uncertainties around the impacts of mining. We further outline requirements for operationalizing quantitative risk assessments in data-poor cases, highlighting the importance of a predictive approach to risk identification. The model can be used to support permitting processes by providing a more comprehensive description of the potential environmental impacts of seabed resource use, allowing iterative updating of the model as new information becomes available.

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Section: Methodsmentioning

confidence: 99%

Causal Approach to Determining the Environmental Risks of Seabed Mining

Kaikkonen

Helle

Kostamo

et al. 2021

Environ. Sci. Technol.

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show abstract

“…For the implementation of the naïve Bayes network, the number of assigned reads and the number of genomic regions per species were discretized (Additional file 1 : table S1) for inputs as parent nodes. Joint probabilities in conditional probability tables (CPTs) of the naive Bayesian network for calculating the likelihood estimate of a taxonomic species label were generated [ 17 ] based on relative weights of influence by the number of assigned reads and coverage of assigned reads, set at 7:3. The network was implemented using a Python library pgmpy 0.1.10 [ 18 ] ( https://pgmpy.org/ ).…”

Section: Methodsmentioning

confidence: 99%

A multi-task CNN learning model for taxonomic assignment of human viruses

Tan

Ban

2021

BMC Bioinformatics

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Background Taxonomic assignment is a key step in the identification of human viral pathogens. Current tools for taxonomic assignment from sequencing reads based on alignment or alignment-free k-mer approaches may not perform optimally in cases where the sequences diverge significantly from the reference sequences. Furthermore, many tools may not incorporate the genomic coverage of assigned reads as part of overall likelihood of a correct taxonomic assignment for a sample. Results In this paper, we describe the development of a pipeline that incorporates a multi-task learning model based on convolutional neural network (MT-CNN) and a Bayesian ranking approach to identify and rank the most likely human virus from sequence reads. For taxonomic assignment of reads, the MT-CNN model outperformed Kraken 2, Centrifuge, and Bowtie 2 on reads generated from simulated divergent HIV-1 genomes and was more sensitive in identifying SARS as the closest relation in four RNA sequencing datasets for SARS-CoV-2 virus. For genomic region assignment of assigned reads, the MT-CNN model performed competitively compared with Bowtie 2 and the region assignments were used for estimation of genomic coverage that was incorporated into a naïve Bayesian network together with the proportion of taxonomic assignments to rank the likelihood of candidate human viruses from sequence data. Conclusions We have developed a pipeline that combines a novel MT-CNN model that is able to identify viruses with divergent sequences together with assignment of the genomic region, with a Bayesian approach to ranking of taxonomic assignments by taking into account both the number of assigned reads and genomic coverage. The pipeline is available at GitHub via https://github.com/MaHaoran627/CNN_Virus.

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“…To quantify the magnitude of impacts between the pressures and the benthic faunal groups, we modelled the BN as an expert system, meaning that no empirical data is directly incorporated in the model. We used the graphical interface provided open source Application for Conditional probability Elicitation (ACE) (Hassall et al 2019) to initialize the conditional probability tables (CPTs) with one expert in geology and one benthic ecologist. The application provides a starting point for defining the overall shape of a conditional probability distribution, which is done by ranking the direction and magnitude of the parent nodes on the child node and populating the table through a scoring algorithm (Hassall et al 2019).…”

Section: Methodsmentioning

confidence: 99%

Causal approach to environmental risks of seabed mining

Kaikkonen

Helle

Kostamo

et al. 2021

Preprint

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Seabed mining is approaching the commercial mining phase across the world's oceans. This rapid industrialization of seabed resource use is introducing new pressures to marine environments. The environmental impacts of such pressures should be carefully evaluated prior to permitting new activities, yet observational data is mostly missing. Here, we examine the environmental risks of seabed mining using a causal, probabilistic network approach. Drawing on a series of interviews with a multidisciplinary group of experts, we outline the cause-effect pathways related to seabed mining activities to inform quantitative risk assessments. The approach consists of (1) iterative model building with experts to identify the causal connections between seabed mining activities and the affected ecosystem components, and (2) quantitative probabilistic modelling to provide estimates of mortality of benthic fauna in the Baltic Sea. The model is used to evaluate alternative mining scenarios, offering a quantitative means to highlight the uncertainties around the impacts of mining. We further outline requirements for operationalizing quantitative risk assessments, highlighting the importance of a cross-disciplinary approach to risk identification. The model can be used to support permitting processes by providing a more comprehensive description of the potential environmental impacts of seabed resource use, allowing iterative updating of the model as new information becomes available.

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Facilitating the elicitation of beliefs for use in Bayesian Belief modelling

Cited by 21 publications

References 22 publications

Causal Approach to Determining the Environmental Risks of Seabed Mining

Causal Approach to Determining the Environmental Risks of Seabed Mining

A multi-task CNN learning model for taxonomic assignment of human viruses

Causal approach to environmental risks of seabed mining

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