User Fee Design by Canadian Municipalities: Considerations Arising from Case Law

User fees have become increasingly relied upon by municipal governments in Canada to fund municipal services due to the combined pressures from federal and provincial devolution of responsibility and the political costs of raising property taxes. While there is a substantial body of literature regarding the rationale for user fees, little information exists about how to design and implement a user fee such that it satisfies the Canadian legal requirements that have been established for the formal classification of user fees. We provide a detailed review of the existing Canadian case law to highlight key legal, technical, and administrative issues that present design and implementation challenges for user fees for Canadian municipalities. Through this analysis we highlight the key legal tests for user fees and discuss their application in case law. The application and interpretation of these tests in the case law draw attention to several unresolved issues and inconsistencies that need to be navigated and resolved by the courts

Minimally invasive biomarkers to detect maternal physiological status in sow saliva and milk

In this study, we aimed to validate existing plasma assays to measure biomarkers for maternal signalling in milk and saliva of lactating sows. These biological samples are minimally invasive to the animal and could give a physiological profile of maternal qualities available to their piglets. Sows were farrowed in a zero-confinement system, and their colostrum and milk samples were manually collected during naturally occurring let-downs (i.e. not induced) over the lactation period. Saliva sampling involved sows voluntarily accepting cotton buds to chew without restraint. Commercial kits designed for blood plasma were tested, and any modifications and results are given. We successfully measured total protein, cortisol, tumour necrosis factor-α (TNF-α) and oxytocin in pig milk and saliva and immunoglobulin G (IgG) in pig milk samples. We were unsuccessful at measuring relaxin and serotonin in these biological samples. We observed higher levels of biomarkers in milk than in saliva. The measurement of TNF-α in pig milk for the first time revealed increased levels with larger litters. This development will allow more detailed understanding of biomarkers in milk. There was also evidence that the minimally invasive technique of using saliva sampling did not interrupt natural oxytocin production around parturition

Facial expression as a potential measure of both intent and emotion

Abstract Facial expressions convey information on emotion, physical sensations, and intent. The much debated theories that facial expressions can be emotions or signals of intent have largely remained separated in animal studies. Here we integrate these approaches with the aim to 1) investigate whether pigs may use facial expressions as a signal of intent and; 2) quantify differences in facial metrics between different contexts of potentially negative emotional state. Facial metrics of 38 pigs were recorded prior to aggression, during aggression and during retreat from being attacked in a dyadic contest. Ear angle, snout ratio (length/height) and eye ratio from 572 images were measured. Prior to the occurrence of aggression, eventual initiators of the first bite had a smaller snout ratio and eventual winners showed a non-significant tendency to have their ears forward more than eventual losers. During aggression, pigs’ ears were more forward orientated and their snout ratio was smaller. During retreat, pigs’ ears were backwards and their eyes open less. The results suggest that facial expressions can communicate aggressive intent related to fight success, and that facial metrics can convey information about emotional responses to contexts involving aggression and fear

Towards on-farm pig face recognition using convolutional neural networks

© 2018 Elsevier B.V. Identification of individual livestock such as pigs and cows has become a pressing issue in recent years as intensification practices continue to be adopted and precise objective measurements are required (e.g. weight). Current best practice involves the use of RFID tags which are time-consuming for the farmer and distressing for the animal to fit. To overcome this, non-invasive biometrics are proposed by using the face of the animal. We test this in a farm environment, on 10 individual pigs using three techniques adopted from the human face recognition literature: Fisherfaces, the VGG-Face pre-trained face convolutional neural network (CNN) model and our own CNN model that we train using an artificially augmented data set. Our results show that accurate individual pig recognition is possible with accuracy rates of 96.7% on 1553 images. Class Activated Mapping using Grad-CAM is used to show the regions that our network uses to discriminate between pigs