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content/applications/call-catalogue.md

@@ -21,5 +21,3 @@ Data used to train artificial intelligence systems come from various sources: fr
 This [catalogue website](https://orca.research.sfu.ca/call-library) shares a curated collection of orca acoustics and annotations which represent the type of samples used in machine learning datasets. The samples here demonstrate the complexity of communication within and between pods.
 
 These samples have been gathered through decades of research by Dr. John Ford, scientist emeritus, and former head of cetacean research at Fisheries and Oceans Canada's Pacific Biological Station in collaboration with Dr. Volker Deecke, professor at the University of Cumbria, UK and James Pilkington from the Department of Ocean and Fisheries (DFO), Canada.
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content/applications/classification.md

@@ -20,4 +20,3 @@ In the GIF below, you can see a model developed by one of our PhD students, Fabi
 All our current and future models are open-source and accessible via our public [GitHub repository](https://github.com/coastal-science/HALLO-models). There you can find instructions, scripts, and configuration files required to train deep learning models at detecting and classifying vocalizations made by Killer whales. Each model comes with a detailed description, including the data used for training.
 
 We are continuously working on developing more advanced models. These forthcoming models aim to classify whale vocalizations with a higher degree of specificity, such as distinguishing between different ecotypes or even identifying individual pods. Stay tuned for these exciting developments.
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content/applications/forecast-models.md

@@ -9,13 +9,10 @@ photocredits: Teng-Wei Lim # or leave blank
 
 ## Forecast Models
 
-Our team is also developing models that will ultimately be able to forecast whale locations based on hydrophone detections, visual observations from citizen science groups, and environmental data. This systems promises not only to alert ship captains and shipping ports when a whale is predicted to cross their path—enabling proactive measures to minimize disturbances—but can also serves as a vital tool for researchers and policymakers. 
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+Our team is also developing models that will ultimately be able to forecast whale locations based on hydrophone detections, visual observations from citizen science groups, and environmental data. This systems promises not only to alert ship captains and shipping ports when a whale is predicted to cross their path—enabling proactive measures to minimize disturbances—but can also serves as a vital tool for researchers and policymakers.
 
 {{< figure url="/img/movement_model_2.png" description="This image illustrates how, from just an initial observation, we can predict a whale's likely path using our extensive archive of historical sightings." credit="Teng-Wei Lim" >}}
 
 The first version of our model, which currently relies solely on visual sightings, was developed by one of our master's students, Teng-Wei Lim. This iteration already enables us to predict the future location of SRKW with a high degree of reliability. Incorporating additional data from hydrophone detections and environmental variables will only enhance the accuracy of these predictions. Read more about it in [this thesis](https://theses.lib.sfu.ca/file/thesis/7791).
 
 {{< figure url="/img/movement_model_1.png" description="This image showcases a kernel density estimate of whale locations, integrating initial sightings to forecast their future positions and infer their present locations." credit="Teng-Wei Lim" >}}
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content/blog/TC_project_2024.md

@@ -28,7 +28,6 @@ In the later months of 2023, a team of researchers in Dr. Ruth Joy’s laborator
 
 {{< figure url="/img/TC_post/thumbnail_IMG_8584.png" class="custom-image" description="Team members at East Point." >}}
 
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 The team analysed how both SRKW and marine vessels use the area of interest off of Saturna Island as well as a spatial analysis of vessel noise in the area and received noise levels to SRKW using a movement model. The team also contributed their own spatial data of vessels in the area of interest, which was collected by lab members over the previous years while stationed at East Point, overlooking the Saturna ISZ.
 
 The results of this project found that keeping the current extent of the Saturna ISZ as a total vessel exclusion zone was most effective. Additionally, a vessel speed restriction zone in the nearby adjacent area of Tumbo Channel could potentially reduce the received underwater noise levels to SRKW. Find the full report [here](https://www.sfu.ca/~rjoy/SaturnaAnalysisOfMeasures_SFU.pdf).
@@ -40,5 +39,3 @@ These regulations are in effect from June 1 – November 30, 2024 and are renewe
 Our hope is that these measures and others help in the recovery of the iconic and treasured SRKW.
 
 {{< figure url="/img/TC_post/thumbnail_19-46-14.jpg" class="custom-image" description="Team members Olivia, Lauren, Rachel and Mikayla at East Point." >}}
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content/research/whale-acoustics.md

@@ -4,13 +4,12 @@ id: "whale-acoustics"
 date: 2023-09-11T18:03:41-07:00
 ---
 
-Whale acoustics is a fascinating subject that explores the vocalizations and communication methods of these creatures. Whales predominantly use sound to communicate, navigate, and interact with their environment. This acoustical signaling plays a crucial role in their survival, from locating prey to maintaining social connections within pods. Through the study of whale acoustics, we are able to gain a deeper understanding of their behavior, social structures, and even migration patterns. This field of research relies on a growing number of underwater listening devices ranging from stationary hydrophone deployments to autonomous gliders that covers vast areas to capture the sounds produced by whales. 
+Whale acoustics is a fascinating subject that explores the vocalizations and communication methods of these creatures. Whales predominantly use sound to communicate, navigate, and interact with their environment. This acoustical signaling plays a crucial role in their survival, from locating prey to maintaining social connections within pods. Through the study of whale acoustics, we are able to gain a deeper understanding of their behavior, social structures, and even migration patterns. This field of research relies on a growing number of underwater listening devices ranging from stationary hydrophone deployments to autonomous gliders that covers vast areas to capture the sounds produced by whales.
 
 However, the sheer volume of data being collected through these passive acoustic systems easily exceeds the capacity of researchers to manually analyze these recordings for relevant vocalizations. In response to this challenge, recent years have seen a surge in the use of artificial intelligence systems designed to automatically recognize and classify various whale vocalizations from acoustic data. These systems not only have the potential to outperform human analysts in terms of accuracy but also significantly accelerates the analysis process, enabling efficient processing of large volumes of data that would otherwise be unfeasible. As a result, marine researchers can redirect their focus towards more complex aspects of whale behavior, ecosystem dynamics and conservation measures, leaving the labor-intensive and tedious recording analysis to advanced AI tools.
 
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 In the HALLO project, our team is interested in developing AI systems powered by deep learning, an advanced subset of machine learning. Deep learning involves the construction and utilization of deep neural networks – complex algorithms that have been outperforming traditional machine learning methods in various fields. These models "learn" a particular task – in our case, identifying whale vocalizations from acoustic recordings – by being exposed to vast amounts of examples (data). In addition, these models can learn and improve over time by seeing new data. This self-improvement characteristic make these algorithms particularly powerful for tasks such as classifying whale sounds in complex and varied acoustic soundscapes.
 
 {{< figure url="/img/SMRUWhaleCalls.png" description="Spectrogram of SRKW vocalizations. A DNN can learn the patterns you see in this image and learn to search for similar ones when given new data." >}}
 
-Our team is developing a whale forecasting system that will leverage the acoustic detections made by our deep neural networks to predict the future locations of whales. The aim is to create an effective warning system that helps prevent ships from colliding with endangered orcas off the coast of British Columbia. More details on this whale forecasting system can be found [here](/research/whale-forecast-system). Meanwhile, you can explore some of the models our team has developed on the [applications page](/applications/) or in our [GitHub repository](https://github.com/coastal-science/HALLO-models).
+Our team is developing a whale forecasting system that will leverage the acoustic detections made by our deep neural networks to predict the future locations of whales. The aim is to create an effective warning system that helps prevent ships from colliding with endangered orcas off the coast of British Columbia. More details on this whale forecasting system can be found [here](/research/whale-forecast-system). Meanwhile, you can explore some of the models our team has developed on the [applications page](/applications/) or in our [GitHub repository](https://github.com/coastal-science/HALLO-models).

content/research/whale-forecast-system.md

@@ -10,4 +10,4 @@ The study of whale forecast systems has garnered significant interest in recent
 
 Our project focuses on developing a sophisticated whale forecast system that leverages visual sightings from citizen science groups, automated acoustic detections from hydrophones, and statistical modeling. By analyzing patterns from whale sightings and acoustic detections, the system can simulate realistic trajectories of SRKW pods. This approach allows us to generate predictions about where these whales are likely to be in the near future. By integrating these forecasts into maritime operations, we can significantly reduce the human impact on these creatures and contribute to their conservation.
 
-This forecasting system can also help researchers and policymakers alike make informed decisions for the protection of whale populations and the preservation of their habitats. A model that relies solely on historical sightings of whales was developed as part of a SFU student master's project, details of which are available in [this thesis](https://theses.lib.sfu.ca/file/thesis/7791). You may also find interesting how this trajectory forecast model has helped guide our team on proposing conservation measures in the Tumbo Channel of the Salish Sea, for reducing the impact of vessel noise on this critical SRKW habitat.
+This forecasting system can also help researchers and policymakers alike make informed decisions for the protection of whale populations and the preservation of their habitats. A model that relies solely on historical sightings of whales was developed as part of a SFU student master's project, details of which are available in [this thesis](https://theses.lib.sfu.ca/file/thesis/7791). You may also find interesting how this trajectory forecast model has helped guide our team on proposing conservation measures in the Tumbo Channel of the Salish Sea, for reducing the impact of vessel noise on this critical SRKW habitat.

content/research/whale-sightings.md

@@ -6,6 +6,6 @@ date: 2023-09-11T18:04:04-07:00
 
 {{< figure url="/img/LucyWhaleSighting.jpg" description="2020 MSc student Lucy Quayle. Photo taken from Saturna Island by Derek Peterson" credit="Derek Peterson" >}}
 
-Whale sightings are one of the ways in which we insight into where the whales are located in real time. On the Canadian side of the border, Master's students from Simon Fraser University have been conducting research on whales in the Southern Gulf Islands and are able to provide whale sightings throughout field seasons over the past several years. These researchers have worked alongside the citizen science group, the Southern Gulf Islands Whale Sightings Network (SGIWSN), made up of over 60 whale sighters located throughout the Southern Gulf Islands. 
+Whale sightings are one of the ways in which we insight into where the whales are located in real time. On the Canadian side of the border, Master's students from Simon Fraser University have been conducting research on whales in the Southern Gulf Islands and are able to provide whale sightings throughout field seasons over the past several years. These researchers have worked alongside the citizen science group, the Southern Gulf Islands Whale Sightings Network (SGIWSN), made up of over 60 whale sighters located throughout the Southern Gulf Islands.
 
 Whale sightings have long been a subject of fascination and intrigue for both researchers and laypeople alike. These majestic creatures, known for their enormous size and mysterious behavior, have captivated the imagination of countless individuals throughout history. With their distinctive features and unique behaviors, whales offer a compelling window into the natural world and the mysteries that lie beneath the surface of our oceans. Whether you are a seasoned researcher or simply a curious observer, the study of whale sightings is sure to offer a wealth of insights and discoveries.

data/carousel/hallo-overview.yaml

@@ -2,6 +2,6 @@ weight: 1
 title: "HALLO"
 description: "<p>Humans and Algorithms<br>Listening to Orcas</p>"
 position: bottom-left # empty (text does not overlap with image), centered, four combinations of {top | bottom}-{left | right}
-textalign:  # left (default), right
+textalign: # left (default), right
 photocredits: "Lauren Laturnus" # or leave blank
 image: "img/LaurenWhaleUnderwater.jpg"

data/carousel/our-research.yaml

@@ -2,6 +2,6 @@ weight: 2
 title: "Our Research"
 description: "<p> We are building a whale forecasting model to protect whales from ship collisions and noise disturbance in the Salish Sea </p>"
 position: top-left # empty (text does not overlap with image), centered, four combinations of {top | bottom}-{left | right}
-textalign:  # left (default), right
+textalign: # left (default), right
 photocredits: "Lucy Quayle" # or leave blank
 image: "img/LucyShipBreach.png"

data/carousel/whale-acoustics.yaml

@@ -2,7 +2,7 @@ weight: 4
 title: "Whale Acoustics"
 description: "<p> Our whale forecast system uses inputs from whale acoustic detections at several underwater hydrophone locations deployed around the Salish Sea </p>"
 position: top-right
-textalign:  # left (default), right
+textalign: # left (default), right
 image: "img/SMRUWhaleCalls.jpg"
 photocredits: "Data from SMRU Consulting" # or leave blank
 href: "research/whale-acoustics"