## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Training and Testing ML and Analyst Methods  
We used the ConservationAI website www.conservationai.co.uk (accessed on 5 December 2019) for ML training and testing using a Faster R-CNN Residual Neural Network (ResNet) 101 Common Objects in Context (COCO) model. We also used transfer learning to reduce the required training datasets. This was performed through the frozen weights of the ResNet101 model and we retrained the fully connected multilayer perceptron (MLP)--a multi-layer neural network connecting each layer in a one-way directed graph. We used the MLP supervised learning technique as it increases model fine-tuning through backpropagation. We trained the model (using the Visual Object Tagging Tool (VoTT) version 1.7.0, Microsoft Corporation, Washington, DC, USA), with a threshold of 1000 images per class to balance all classes, preventing classification bias within the model.  
Firstly, we extracted the human training dataset from the experimental footage and collated the wildlife training dataset using images from the extracted CT footage, iNaturalist, and the Endangered Wildlife Trust (EWT). Secondly, we used between 100-200 images for each factor combination for both human and wildlife training datasets. Lastly, we sorted the wildlife (16,333 images) and human (11,781 images) testing datasets into four copies, one for the ML model and three for the analysts (citizen scientists) (see Appendix B Table A1 for training and testing dataset counts per class).  
Once we trained the automated model on the training dataset, we uploaded the testing dataset to the ConservationAI website. The algorithm then processed all images for wildlife and human detection and classification instances. For analyst (citizen scientist) training,we obtained three images per species from iNaturalist. We then distributed them to the three analysts for two days to memorise their features and associated classification labels. Following this, we provided the testing datasets in a random order to prevent pattern recognition and inference of the target location within the image based on a previous location. We used the Google images app, version 5.7.0.327437691, Microsoft Corporation, Washington, DC, USA, for the analyst detection and classification counts. The process involved drawing a bounding box over the target's parameters--the outer edge of the object's body, and typing the proposed classification name within the attached comments box, as explained below.  
After analyst and ML detection, we processed the images, resulting in the following potential options: (1) false-negative detection and no classification, (2) true-positive detection and false-negative classification, or (3) true-positive detection and classification (see Tables 2 and 3 for detection and classification performance results). The definition of such options is as follows--a bounding box surrounding the target's parameters would be a true-positive detection; the correct labelling/classification of the detected target would be a true-positive detection and classification; if there is no bounding box or it is not surrounding the target's parameters, it is a false-negative detection and no classification; if the target is detected but mislabelled as another class, it is a true-positive detection and false-negative classification.  
We proceeded to categorise datasets in Microsoft Excel Office 365 version 1.0.1 into combinations of the variables included within the image and sorted them into their specific models. We gave the CC variable a not applicable (na) value in the event of a false-negative detection response, and we stacked the analyst data with corresponding number labels to analyse the three analyst response results simultaneously, assessing between analyst differences.