Ito, Stratonovich and Friends

Jouanne-Diedrich, Holger von

doi:10.2139/ssrn.2956257

Cited by 2 publications

(2 citation statements)

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“…OneR uses one feature to generate a single rule (basically a decision tree with a single split; Holte, 1993 ; Parsania et al, 2014 ). OneR was developed as a baseline learner; for more complex, less-interpretable learners to be useful that learner should perform better than the OneR model ( Holte, 1993 ; von Jouanne-Diedrich, 2017 ). In the current study minbucket was the only parameter tuned when implementing OneR.…”

Section: Methodsmentioning

confidence: 99%

Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production

Weller

Love

Belias

et al. 2020

Front. Sustain. Food Syst.

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While the Food Safety Modernization Act established standards for the use of surface water for produce production, water quality is known to vary over space and time. Targeted approaches for identifying hazards in water that account for this variation may improve growers' ability to address pre-harvest food safety risks. Models that utilize publicly-available data (e.g., land-use, real-time weather) may be useful for developing these approaches. The objective of this study was to use pre-existing datasets collected in 2017 (N = 181 samples) and 2018 (N = 191 samples) to train and test models that predict the likelihood of detecting Salmonella and pathogenic E. coli markers (eaeA, stx) in agricultural water. Four types of features were used to train the models: microbial, physicochemical, spatial and weather. "Full models" were built using all four features types, while "nested models" were built using between one and three types. Twenty learners were used to develop separate full models for each pathogen. Separately, to assess information gain associated with using different feature types, six learners were randomly selected and used to develop nine, nested models each. Performance measures for each model were then calculated and compared against baseline models where E. coli concentration was the sole covariate. In the methods, we outline the advantages and disadvantages of each learner. Overall, full models built using ensemble (e.g., Node Harvest) and "blackbox" (e.g., SVMs) learners out-performed full models built using more interpretable learners (e.g., tree-and rule-based learners) for both outcomes. However, nested eaeAstx models built using interpretable learners and microbial data performed almost as well as these full models. While none of the nested Salmonella models performed as well as the full models, nested models built using spatial data consistently outperformed models that excluded spatial data. These findings demonstrate that machine learning approaches can be used to predict when and where pathogens are likely to be present in agricultural water. This study serves as a proof-of-concept that can be built upon once larger datasets become available and provides guidance on Weller et al. Predicting Foodborne Pathogen Presence in Water the learner-data combinations that should be the foci of future efforts (e.g., tree-based microbial models for pathogenic E. coli).

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Section: Methodsmentioning

confidence: 99%

Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production

Weller

Love

Belias

et al. 2020

Front. Sustain. Food Syst.

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show abstract

“…Otters were also classified into size categories based on their total length (nose to tail tip) using the “bins” function in R (OneR v2.2 package; von Jouanne‐Diedrich, 2017 ), which applies a clustering method using Jenks natural breaks optimization. Male and female otters were clustered separately into small (males <1046 mm, females <936 mm long), medium (males between 1046 mm and 1131 mm, females between 936 mm and 1031 mm), and large (males >1131 mm, females >1031 mm).…”

Section: Methodsmentioning

confidence: 99%

Otterly delicious: Spatiotemporal variation in the diet of a recovering population of Eurasian otters (Lutra lutra) revealed throughDNAmetabarcoding and morphological analysis of prey remains

Drake

Cuff

Bedmar

et al. 2023

Ecology and Evolution

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Eurasian otters are apex predators of freshwater ecosystems and a recovering species across much of their European range; investigating the dietary variation of this predator over time and space, therefore, provides opportunities to identify changes in freshwater trophic interactions and factors influencing the conservation of otter populations. Here we sampled feces from 300 dead otters across England and Wales between 2007 and 2016, conducting both morphological analyses of prey remains and dietary DNA metabarcoding. Comparison of these methods showed that greater taxonomic resolution and breadth could be achieved using DNA metabarcoding but combining data from both methodologies gave the most comprehensive dietary description. All otter demographics exploited a broad range of taxa and variation likely reflected changes in prey distributions and availability across the landscape. This study provides novel insights into the trophic generalism and adaptability of otters across Britain, which is likely to have aided their recent population recovery, and may increase their resilience to future environmental changes.

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Ito, Stratonovich and Friends

Cited by 2 publications

References 0 publications

Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production

Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production

Otterly delicious: Spatiotemporal variation in the diet of a recovering population of Eurasian otters (Lutra lutra) revealed throughDNAmetabarcoding and morphological analysis of prey remains

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