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Title:          Tracking Fish Abundance by Underwater Image Recognition
Subject:        Scientific Reports, doi:10.1038/s41598-018-32089-8
Keywords:       
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     Item: 
      Title: Tracking Fish Abundance by Underwater Image Recognition
      Children: 
       Item: 
        Title: Results
        Children: 
         Item: 
          Title: Training and Validation of the Image Classifier. 
         Item: 
          Title: Test of the Image Classifier. 
          Children: 
           Item: 
            Title: The effects of turbidity and bio-fouling on the automated recognition performance. 
         Item: 
          Title: Efficacy of the Automated Recognition for Ecological Analyses. 
       Item: 
        Title: Discussion
       Item: 
        Title: Methods
        Children: 
         Item: 
          Title: The Image Dataset. 
         Item: 
          Title: The Environmental Data. 
         Item: 
          Title: Image Segmentation and Feature Extraction. 
         Item: 
          Title: Image Recognition and Feature Selection. 
         Item: 
          Title: Ecological Statistical Analysis. 
       Item: 
        Title: Acknowledgements
       Item: 
        Title: Figure 1 Examples of the most relevant conditions of image acquisition occurring at the OBSEA observatory during the daylight and night: clear and turbid water, bio-fouling on the camera housing, crowded scenes, and wrong pan-tilt-zoom camera positions an
       Item: 
        Title: Figure 2 Representative Regions of Interest (RoIs) used in the examples set for training the binary image classifier.
       Item: 
        Title: Figure 3 An example of a correlation between the time-series obtained through the visual inspection of the images (red line) and the time-series automatically extracted by the image classifier (blue line).
       Item: 
        Title: Figure 4 Examples of automatic recognition in different operating conditions: Sunlight with turbid waters (b), (l) twilight (c) presence of mucilage (e), (f) bio-fouling over the camera (i).
       Item: 
        Title: Figure 5 Pearson correlation between the observed and the recognised time series as a function of the level of water turbidity and bio-fouling on the camera housing.
       Item: 
        Title: Table 1 Summary of the data acquired in the year 2012 that was used to train and validate the binary image classifier.
       Item: 
        Title: Table 2 Results of Univariate PERMANOVA test carried out on factor “day vs.
       Item: 
        Title: Table 3 Main PERMANOVA test and pairwise comparisons (only significant variations are shown) of observed and recognized abundance data by month, regarding absent or small quantity of bio-fouling (F = 0) and different combinations of turbidity score (T).
       Item: 
        Title: Table 4 Results of GLM models for absent or small quantity of bio-fouling (F = 0), and different values of water turbidity (T).
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The attribute platform is optionally allowed for situations in which multiple URLs must be specified. PRISM recommends that a subset of the PCV platform values, namely “mobile” and “web”, be used in conjunction with this element. 
NOTE: PRISM recommends against the use of the #other value allowed in the PRISM Platform controlled vocabulary. In lieu of using #other please reach out to the PRISM group at prism-wg@yahoogroups.com to request addition of your term to the Platform Controlled Vocabulary. 
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SMUR = Submitted Manuscript Under Review
AM = Accepted Manuscript
P = Proof
VoR = Version of Record
CVoR = Corrected Version of Record
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