Climate surprises - how do we prepare?
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“Nothing is so painful to the human mind as a great and sudden change.” —Mary Shelley 1818
Ocean Data Justice Posts
2024
2023
Fitting communities into forecast system topologies
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“Does the removal of urchins and perrywinkles lead to brown water?” This question is one of hundreds of hypotheses I’ve encountered working with community groups on forecasting projects. It’s one of the reasons that community-centered science is fun. The ideas I encounter in communities are far more wide ranging than what’s found among scientists, who are also interesting, but are often much more in lock-step with each other (McClenachan et al. 2022).
2022
Non-profit publishers and citational politics
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This week we discussed this article on citational politics. There’s a lot to unpack, from algorithmic bias in search engines to the motivation for “firsting” in science. The article also links to additional blog posts that delve into some of these topics. The further you go, the more you see how this problem is entangled with the other inequities that surround us. In our conversation yesterday, one question that came up was a pragmatic one— when doing literature searches to write papers, how can we use search engines to do what CLEAR calls “citing differently”?
Forget 2100
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What will the world look like in the year 2100?
Will my Scientist job be replaced by a bot?
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I read an unsettling tweet the other day. Yes, many boring conversations start with that sentence, but hear me out. The tweeter suggested that in the near future, all jobs will be better performed by artificial intelligence, or “AI”. This is part of the promise and the fear of AI–that we will all be replaced by bots. And yes, it’s possible that the tweet itself came from a bot, and not a human, but it started me down a path of wondering: How close is my job to being replaced by AI?
Benefits and challenges of a stakeholder-driven shellfish toxicity forecast in coastal maine
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Paralytic shellfish poison (PSP) is a human health concern for shellfish aquaculture and wild harvest. This paper discusses lessons learned from a forecasting program for PSP in coastal Maine, USA, designed based on stakeholder input, and run in an operational mode for the 2021 season. The forecast uses a deep learning algorithm to make site-specific, probabilistic forecasts at a weekly forecast range for toxin levels measured in shellfish tissue. Forecasts had high accuracy in the 2021 season, correctly predicting closure events and locations despite a highly unusual season. Stakeholders reported a positive view of the forecast system, and stakeholder input continues to be of key importance as further modifications are made to the system. There are benefits and challenges to the stakeholder-based design of the system.
Toxic algae closures: What kinds of predictions are needed?
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When I was growing up we had weather forecasts, and it was great. You could either plan your day or just complain about how wrong the weather forecast was (or a bit of both). Now, there are forecasts for almost everything one could monitor, from pollen forecasts to election forecasts to covid forecasts. There is even something called “predictive policing”–a forecast for potential criminal activity before it happens. Artificial intelligence, networked data, and rapidly advancing science has made it tempting to forecast many things and to deliver the predictions on nice websites and “data dashboards”. Harmful and toxic algae are no exception, and as aquaculture becomes more and more essential to feeding the world, forecasts for harmful blooms are poised to have more and more utility.