173 deploy pull request builds to e4e devucsdedu

.github/workflows/jekyll.yml

@@ -107,7 +107,7 @@ jobs:
           echo "$SSH_PRIVATE_KEY" > $SSH_KEY_PATH
           sudo chmod 600 $SSH_KEY_PATH
           echo "$SSH_KNOWN_HOSTS" > ~/.ssh/known_hosts
-          wget https://raw.githubusercontent.com/UCSD-E4E/website2.0/main/_deploy_e4e-dev.sh
-          scp -i $SSH_KEY_PATH _deploy_e4e-dev.sh [email protected]:/tmp/deploy_e4e-dev.sh
-          ssh -i $SSH_KEY_PATH [email protected] '/bin/bash /tmp/deploy_e4e-dev.sh'
+          wget https://raw.githubusercontent.com/UCSD-E4E/website2.0/main/_deploy_e4e.sh
+          scp -i $SSH_KEY_PATH _deploy_e4e.sh [email protected]:/tmp/deploy_e4e.sh
+          ssh -i $SSH_KEY_PATH [email protected] '/bin/bash /tmp/deploy_e4e.sh'
 

.github/workflows/pr_initial_deploy.yml

@@ -0,0 +1,28 @@
+name: Deploy Jekyll PR to E4E-DEV
+
+on:
+    pull_request: 
+        types: 
+            - opened
+            - edited
+        branches: 
+            - main
+    workflow_dispatch: 
+
+jobs:
+    deploy:
+        runs-on: ubuntu-latest
+        steps:
+            - name: Execute Deploy
+              env:
+                SSH_PRIVATE_KEY: ${{secrets.KASTNER_ML_DEPLOY_SSH}}
+                SSH_KNOWN_HOSTS: ${{secrets.KASTNER_ML_KNOWN_HOSTS}}
+                SSH_KEY_PATH: ${{github.workspace}}/../private.key
+                PR_NUMBER: ${{github.event.number}}
+              run: |
+                mkdir -p ~/.ssh/
+                echo "$SSH_PRIVATE_KEY" > $SSH_KEY_PATH
+                chmod 600 $SSH_KEY_PATH
+                echo "$SSH_KNOWN_HOSTS" > ~/.ssh/known_hosts
+                # ssh -i $SSH_KEY_PATH [email protected] '/bin/bash /tmp/deploy_e4e.sh'
+

_bibliography/onboarding_papers/acoustic_species_id.bib

@@ -5,7 +5,7 @@ @InProceedings{Ayers2021
   year      = {2021},
   month     = {July},
   volume    = {38},
-  abstract  = {The acoustic signature of a natural soundscape can reveal consequences of climate change on biodiversity. Hardware costs, human labor time, and expertise dedicated to labeling audio are impediments to conducting acoustic surveys across a representative portion of an ecosystem. These barriers are quickly eroding away with the advent of low-cost, easy to use, open source hardware and the expansion of the machine learning field providing pre-trained neural networks to test on retrieved acoustic data. One consistent challenge in passive acoustic monitoring (PAM) is a lack of reliability from neural networks on audio recordings collected in the field that contain crucial biodiversity information that otherwise show promising results from publicly available training and test sets. To demonstrate this challenge, we tested a hybrid recurrent neural network (RNN) and convolutional neural network (CNN) binary classifier trained for bird presence/absence on two Peruvian bird audiosets. The RNN achieved an area under the receiver operating characteristics (AUROC) of 95% on a dataset collected from Xeno-canto and Google’s AudioSet ontology in contrast to 65% across a stratified random sample of field recordings collected from the Madre de Dios region of the Peruvian Amazon. In an attempt to alleviate this discrepancy, we applied various audio data augmentation techniques in the network’s training process which led to an AUROC of 77% across the field recordings},
+  abstract  = {The acoustic signature of a natural soundscape can reveal consequences of climate change on biodiversity. Hardware costs, human labor time, and expertise dedicated to labeling audio are impediments to conducting acoustic surveys across a representative portion of an ecosystem. These barriers are quickly eroding away with the advent of low-cost, easy to use, open source hardware and the expansion of the machine learning field providing pre-trained neural networks to test on retrieved acoustic data. One consistent challenge in passive acoustic monitoring (PAM) is a lack of reliability from neural networks on audio recordings collected in the field that contain crucial biodiversity information that otherwise show promising results from publicly available training and test sets. To demonstrate this challenge, we tested a hybrid recurrent neural network (RNN) and convolutional neural network (CNN) binary classifier trained for bird presence/absence on two Peruvian bird audiosets. The RNN achieved an area under the receiver operating characteristics (AUROC) of 95% on a dataset collected from Xeno-canto and Google's AudioSet ontology in contrast to 65% across a stratified random sample of field recordings collected from the Madre de Dios region of the Peruvian Amazon. In an attempt to alleviate this discrepancy, we applied various audio data augmentation techniques in the network's training process which led to an AUROC of 77% across the field recordings},
   url       = {https://www.climatechange.ai/papers/icml2021/14},
 }
 

_bibliography/onboarding_papers/fishsense.bib

@@ -5,7 +5,7 @@ @INPROCEEDINGS{tueller_maddukuri_paxson_et_al_oceans_2021
 	year = {2021},
 	month={September},
 	publisher={IEEE},
-	abstract={There is a need for reliable underwater fish monitoring systems that can provide oceanographers and researchers with valuable data about life underwater. Most current methods rely heavily on human observation which is both error prone and costly. FishSense provides a solution that accelerates the use of depth cameras underwater, opening the door to 3D underwater imaging that is fast, accurate, cost effective, and energy efficient. FishSense is a sleek handheld underwater imaging device that captures both depth and color images. This data has been used to calculate the length of fish, which can be used to derive biomass and health. The FishSense platform has been tested through two separate deployments. The first deployment imaged a toy fish of known length and volume within a controlled testing pool. The second deployment was conducted within an 70,000 gallon aquarium tank with multiple species of fish. A Receiver Operating Characteristic (ROC) curve has been computed based on the detector’s performance across all images, and the mean and standard deviation of the length measurements of the detections has been computed.},
+	abstract={There is a need for reliable underwater fish monitoring systems that can provide oceanographers and researchers with valuable data about life underwater. Most current methods rely heavily on human observation which is both error prone and costly. FishSense provides a solution that accelerates the use of depth cameras underwater, opening the door to 3D underwater imaging that is fast, accurate, cost effective, and energy efficient. FishSense is a sleek handheld underwater imaging device that captures both depth and color images. This data has been used to calculate the length of fish, which can be used to derive biomass and health. The FishSense platform has been tested through two separate deployments. The first deployment imaged a toy fish of known length and volume within a controlled testing pool. The second deployment was conducted within an 70,000 gallon aquarium tank with multiple species of fish. A Receiver Operating Characteristic (ROC) curve has been computed based on the detector's performance across all images, and the mean and standard deviation of the length measurements of the detections has been computed.},
 	url={https://agu.confex.com/agu/OVS21/meetingapp.cgi/Paper/787405}}
 @ARTICLE{wong_humphrey_switzer_wuwnet_2022,
   author = {Wong, Emily and Humphrey, Isabella and Switzer, Scott and Crutchfield, Christopher and Hui, Nathan and Schurgers, Curt and Kastner, Ryan},

_bibliography/onboarding_papers/radio_telemetry_tracking.bib

@@ -3,7 +3,7 @@ @Article{Hui2021
   journal  = {Journal of Field Robotics},
   title    = {A more precise way to localize animals using drones},
   year     = {2021},
-  abstract = {Abstract Radio telemetry is a commonly used technique in conservation biology and ecology, particularly for studying the movement and range of individuals and populations. Traditionally, most radio telemetry work is done using handheld directional antennae and either direction-finding and homing techniques or radio-triangulation techniques. Over the past couple of decades, efforts have been made to utilize unmanned aerial vehicles to make radio-telemetry tracking more efficient, or cover more area. However, many of these approaches are complex and have not been rigorously field-tested. To provide scientists with reliable quality tracking data, tracking systems need to be rigorously tested and characterized. In this paper, we present a novel, drone-based, radio-telemetry tracking method for tracking the broad-scale movement paths of animals over multiple days and its implementation and deployment under field conditions. During a 2-week field period in the Cayman Islands, we demonstrated this system's ability to localize multiple targets simultaneously, in daily 10 min tracking sessions over a period of 2 weeks, generating more precise estimates than comparable efforts using manual triangulation techniques.},
+  abstract = {Abstract Radio telemetry is a commonly used technique in conservation biology and ecology, particularly for studying the movement and range of individuals and populations. Traditionally, most radio telemetry work is done using handheld directional antennae and either direction-finding and homing techniques or radio-triangulation techniques. Over the past couple of decades, efforts have been made to utilize unmanned aerial vehicles to make radio-telemetry tracking more efficient, or cover more area. However, many of these approaches are complex and have not been rigorously field-tested. To provide scientists with reliable quality tracking data, tracking systems need to be rigorously tested and characterized. In this paper, we present a novel, drone-based, radio-telemetry tracking method for tracking the broad-scale movement paths of animals over multiple days and its implementation and deployment under field conditions. During a 2-week field period in the Cayman Islands, we demonstrated this system's ability to localize multiple targets simultaneously, in daily 10 min tracking sessions over a period of 2 weeks, generating more precise estimates than comparable efforts using manual triangulation techniques.},
   doi      = {https://doi.org/10.1002/rob.22017},
   keywords = {aerial robotics, environmental monitoring, exploration, rotorcraft},
   url      = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.22017},

_bibliography/onboarding_papers/smartfin.bib

@@ -9,7 +9,7 @@ @article{bresnehan_cyronak_brewin_et_al_csr_2022
 	url = {https://www.sciencedirect.com/science/article/pii/S0278434322001029},
 	author = {Philip Bresnahan and Tyler Cyronak and Robert J.W. Brewin and Andreas Andersson and Taylor Wirth and Todd Martz and Travis Courtney and Nathan Hui and Ryan Kastner and Andrew Stern and Todd McGrain and Danica Reinicke and Jon Richard and Katherine Hammond and Shannon Waters},
 	keywords = {Coastal oceanography, Citizen science, Surfing, Sea surface temperature, Outreach},
-	abstract = {Coastal populations and hazards are escalating simultaneously, leading to an increased importance of coastal ocean observations. Many well-established observational techniques are expensive, require complex technical training, and offer little to no public engagement. Smartfin, an oceanographic sensor–equipped surfboard fin and citizen science program, was designed to alleviate these issues. Smartfins are typically used by surfers and paddlers in surf zone and nearshore regions where they can help fill gaps between other observational assets. Smartfin user groups can provide data-rich time-series in confined regions. Smartfin comprises temperature, motion, and wet/dry sensing, GPS location, and cellular data transmission capabilities for the near-real-time monitoring of coastal physics and environmental parameters. Smartfin's temperature sensor has an accuracy of 0.05 °C relative to a calibrated Sea-Bird temperature sensor. Data products for quantifying ocean physics from the motion sensor and additional sensors for water quality monitoring are in development. Over 300 Smartfins have been distributed around the world and have been in use for up to five years. The technology has been proven to be a useful scientific research tool in the coastal ocean—especially for observing spatiotemporal variability, validating remotely sensed data, and characterizing surface water depth profiles when combined with other tools—and the project has yielded promising results in terms of formal and informal education and community engagement in coastal health issues with broad international reach. In this article, we describe the technology, the citizen science project design, and the results in terms of natural and social science analyses. We also discuss progress toward our outreach, education, and scientific goals.}
+	abstract = {Coastal populations and hazards are escalating simultaneously, leading to an increased importance of coastal ocean observations. Many well-established observational techniques are expensive, require complex technical training, and offer little to no public engagement. Smartfin, an oceanographic sensor-equipped surfboard fin and citizen science program, was designed to alleviate these issues. Smartfins are typically used by surfers and paddlers in surf zone and nearshore regions where they can help fill gaps between other observational assets. Smartfin user groups can provide data-rich time-series in confined regions. Smartfin comprises temperature, motion, and wet/dry sensing, GPS location, and cellular data transmission capabilities for the near-real-time monitoring of coastal physics and environmental parameters. Smartfin's temperature sensor has an accuracy of 0.05 °C relative to a calibrated Sea-Bird temperature sensor. Data products for quantifying ocean physics from the motion sensor and additional sensors for water quality monitoring are in development. Over 300 Smartfins have been distributed around the world and have been in use for up to five years. The technology has been proven to be a useful scientific research tool in the coastal ocean—especially for observing spatiotemporal variability, validating remotely sensed data, and characterizing surface water depth profiles when combined with other tools—and the project has yielded promising results in terms of formal and informal education and community engagement in coastal health issues with broad international reach. In this article, we describe the technology, the citizen science project design, and the results in terms of natural and social science analyses. We also discuss progress toward our outreach, education, and scientific goals.}
 }
 
 @Misc{current_efforts,