otter

cross-posted from: https://lemmy.ca/post/23884006

Link to full text study:

https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00094-3/fulltext

Background Cooling towers containing Legionella spp are a high-risk source of Legionnaires’ disease outbreaks. Manually locating cooling towers from aerial imagery during outbreak investigations requires expertise, is labour intensive, and can be prone to errors. We aimed to train a deep learning computer vision model to automatically detect cooling towers that are aerially visible.

Methods Between Jan 1 and 31, 2021, we extracted satellite view images of Philadelphia (PN, USA) and New York state (NY, USA) from Google Maps and annotated cooling towers to create training datasets. We augmented training data with synthetic data and model-assisted labelling of additional cities. Using 2051 images containing 7292 cooling towers, we trained a two-stage model using YOLOv5, a model that detects objects in images, and EfficientNet-b5, a model that classifies images. We assessed the primary outcomes of sensitivity and positive predictive value (PPV) of the model against manual labelling on test datasets of 548 images, including from two cities not seen in training (Boston [MA, USA] and Athens [GA, USA]). We compared the search speed of the model with that of manual searching by four epidemiologists.

Link to full text study:

https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00094-3/fulltext

Background Cooling towers containing Legionella spp are a high-risk source of Legionnaires’ disease outbreaks. Manually locating cooling towers from aerial imagery during outbreak investigations requires expertise, is labour intensive, and can be prone to errors. We aimed to train a deep learning computer vision model to automatically detect cooling towers that are aerially visible.

Methods Between Jan 1 and 31, 2021, we extracted satellite view images of Philadelphia (PN, USA) and New York state (NY, USA) from Google Maps and annotated cooling towers to create training datasets. We augmented training data with synthetic data and model-assisted labelling of additional cities. Using 2051 images containing 7292 cooling towers, we trained a two-stage model using YOLOv5, a model that detects objects in images, and EfficientNet-b5, a model that classifies images. We assessed the primary outcomes of sensitivity and positive predictive value (PPV) of the model against manual labelling on test datasets of 548 images, including from two cities not seen in training (Boston [MA, USA] and Athens [GA, USA]). We compared the search speed of the model with that of manual searching by four epidemiologists.

🌎 Jun 26, 2024 🌍
🔥 1 | Avg. Guesses: 11.5
🟧🟨🟨⬜⬜🟥🟧🟧
🟧🟧🟧🟧🟨🟥🟥🟥
🟥🟥🟥🟥🟩 = 21

https://globle-game.com
#globle

I did not do as well this time

cross-posted from: https://lemmy.ca/post/23814659

You can see the following discussions for more context:

• https://reddthat.com/post/20220167
• https://mander.xyz/post/14378124

cross-posted from: https://lemmy.ca/post/23518886

Dory is the first pup to be born at the Marine Mammal Rescue Centre. Her mother Donnelly was admitted in critical condition with severe injuries. Staff later discovered that she was pregnant and worked hard to safe both, the mother and her unborn pup. Dory was born 2 months after Donnelly's admission to the rescue centre.

Full details, photos, videos, and live tracking link: https://mmrpatients.org/patient/pv2158-dory/

Patient Profile:

Species: Harbour Seal
Patient ID: PV2158
Admitted on: 2021/07/20
Collection Site: Born in Rockfish pool, MMR
Reason for Admission: Born at the rescue centre
Weight at Admission: 8.4 kg
Patient Status: released
Time in Care: 89 days (2 months, 4 weeks)

Link to profile: https://mmrpatients.org/patient/cc2401-moira/

Story

On February 4, 2024, our team was alerted to a loggerhead sea turtle in distress, found floating in a kelp bed in the cold waters near Pedder Bay, about 40 minutes from Victoria, BC. The turtle, now nicknamed Moira, was in a critical state when discovered, prompting an immediate response from local authorities and marine biologists. This incident marks only the second time a loggerhead sea turtle has been reported in BC waters, highlighting the rarity of such events and the importance of swift conservation efforts. Loggerhead sea turtles are a federally protected species in the USA, listed as threatened under the Endangered Species Act since 1978, underscoring the significance of Moira's rescue and rehabilitation. Learn more about her rescue story here: https://vammr.org/rescue-news/moiras-rescue/

Name

Moira was named after Moira Rose, a fictional character in the award-winning Canadian television show Schitt's Creek.

Patient Record (as of 2024-06-23)

Species: Loggerhead Sea Turtle
Patient ID: CC2401
Admitted on: 2024/02/04
Collection Site: Pedder Bay, BC
Reason for Admission: Cold-shocked and Hyperthermic
Weight at Admission: 36.22 kg
Patient Status: in care
Time in Care: 140 days (4 months, 2 weeks, 4 days)
Current Habitat: In Quarantine 

Strands #111
“I love the nightlife”
💡🔵🔵💡
🔵💡🔵🔵
🟡🔵