Kristen Pol

Proposal to replace "no jargon" with "be precise" as a cardinal rule for great #SciComm: 1. It intuitively prompts a circular workflow that includes evaluation, because... 2. "Precision" changes in context 3. It promotes equity 4. It tells you what to do instead This proposal prompted by a Bluesky post by Arielle Duhaime-Ross that I'll use as an example of why. Implied in their post (screenshotted below) is that saying "COVID is over" = saying "I don't recognize the harm it's still doing". Using "be precise" as a foundation for good comms gets us to: 1. Is implying "COVID is over" actually what we mean to say? 2. Maybe not; if our audience is interested in COVID protection policies, they're probably still concerned about COVID! 3. Rejecting this phrasing will help build a narrative that people are still at risk and protection policies would still do good (but if you're in a hostile system, you can argue for precise language with the justification that saying "during lockdowns" or similar elicits a different visceral emotional reaction than saying "during COVID", which is also true!) 4. So then, what exactly do we mean if not "during COVID"? Let's say that instead. "No jargon", as a rule, doesn't trigger any particular examination of this kind of phrasing, while "be precise" does.

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🌎Local Climate Action That Works for Every Jurisdiction 💪 California’s 2022 Scoping Plan Appendix D identifies three priority areas where local governments can take meaningful climate action—regardless of size, resources, or existing programs. These “no-regret” strategies align with state climate goals while offering flexibility for implementation: 1. Transportation Electrification 🚗 • Convert municipal fleets to zero-emission vehicles • Streamline EV charging infrastructure permitting • Exceed state building codes for EV readiness 2. VMT Reduction 🚲 • Eliminate or reduce minimum parking standards • Implement Complete Streets policies • Increase density near transit • Enable mixed-use, walkable development 3. Building Decarbonization 🔋 • Adopt all-electric new construction codes • Create retrofit programs for existing buildings • Deploy renewable energy and storage on public facilities What makes this framework powerful: jurisdictions can choose strategies that fit their specific context while contributing to statewide emission reductions. A small rural city and a large urban center can both take action—just in different ways. For cities without resources for full Climate Action Plans, these priorities provide a clear roadmap. For jurisdictions with existing plans, they offer alignment with California’s 2030 and 2045 targets. Link to full document: https://lnkd.in/gkMqdDPY #ClimateAction #LocalGovernment #Sustainability #CaliforniaClimate #UrbanPlanning

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A new report called “Changing the Game: Community-based strategies and climate mitigation”—from Marin Community Foundation, the John D. and Catherine T. MacArthur Foundation, and Equation Campaign—highlights how community-based strategies are more cost-effective climate mitigation solutions than big technical solutions like carbon capture. ✨ The report found that the cost per community-based effort is well under $1 of philanthropic investment per MTCO2e mitigated through 2030. ✨ This emphasizes what we have highlighted through CLIMA Fund's #SoilToSky reports, and what CLIMA’s executive director, Lindley Mease, aptly captures in this article: “Grassroots climate solutions — those coming from the communities most impacted by the climate crisis — are not only effective, they’re inherently democratic, locally grounded and globally significant…In this moment, mitigation funders have an opportunity to back strategies that are both deeply transformative and measurably impactful.” Article via Michael Kavate and Inside Philanthropy: https://lnkd.in/erDsdXAJ

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🧩 What happens when we put AlphaFold2’s predictions to the ultimate test—across species and structure types? This study delivers the first comprehensive benchmark of AlphaFold2’s ability to predict amino acid-level secondary structure (SS) and solvent accessibility (SA) across five major taxonomic groups. The findings show that AlphaFold2 not only excels in 3D structure prediction but also outperforms leading sequence-based predictors in residue-level accuracy! Here are the 10 key takeaways from the paper: 👇 1️⃣ Outstanding Accuracy: AlphaFold2 achieves 92.8% accuracy (Q3) in SS prediction and 0.815 Pearson correlation in SA prediction — the highest across all tested methods. 2️⃣ Broad Taxonomic Coverage: Evaluated on nearly 10,000 proteins spanning animals, plants, fungi, bacteria, and archaea for a robust cross-species analysis. 3️⃣ Region-Level Superiority: AF2 predictions closely mimic native SS region distributions, excelling in identifying region sizes and numbers. 4️⃣ Statistically Significant Gains: AF2 significantly outperforms PsiPred, NetSurfP-3.0, SPOT-1D-Single, and ASAquick across all metrics (p < 0.05). 5️⃣ Low Error Rates: Shows minimal confusion between helices and strands (Qerror = 0.001), indicating highly reliable SS classification. 6️⃣ Robust Across Similarity Gaps: AF2 maintains high performance even for proteins with low sequence similarity to training data. 7️⃣ Segment Overlap Excellence: Achieves top SOV scores for helix, strand, and coil regions, demonstrating superior alignment with native SS regions. 8️⃣ Threshold-Independent SA Accuracy: Maintains strong binary SA prediction (buried vs. exposed) across multiple thresholds (ACC ≈ 88%). 9️⃣ Validation with Pre-Computed Structures: Leverages the AlphaFoldDB’s >200M pre-computed structures, making these insights highly accessible. 🔟 Implications for Drug Discovery & Annotation: Facilitates fast, accurate annotation of protein functions at scale — especially valuable where experimental data is scarce. 📝 Jing Yu, Bi Zhao, Lukasz Kurgan. 🔗 Comprehensive assessment of AlphaFold’s predictions of secondary structure and solvent accessibility at the amino acid-level in eukaryotic, bacterial and archaeal proteins. Computational and Structural Biotech Journal, DOI: https://lnkd.in/gARR_iN6 📚 CSBJ: https://www.csbj.org/ Virginia Commonwealth University, University of South Florida

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How do you communicate the idea of a heat wave in an image? Positive images of heat waves actually REDUCE worry about extreme heat. This study has actionable insights for climate communicators across media - for instance: 1. Positive images reduce the impacts of messages about extreme heat. 2. Avoid “fun in the sun” images when communicating about extreme heat. 3. Message effects were similar across political parties. Read more: https://lnkd.in/ez4g4K8d

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Carbon Storage’s Prudent Limit: The End Of Infinite Assumptions Nature just published "A prudent planetary limit for geologic carbon storage". It’s an important correction to decades of CCS optimism. CleanTechnica article: https://lnkd.in/gwNfyzJC For years, storage was treated as if it were limitless, with estimates in the tens of thousands of gigatons. That assumption allowed policy models to treat CCS as both a lifeline for fossil fuels and a safety valve for overshoot. The new work applies risk filters — seismic zones, depth limits, shallow waters, urban buffers, protected areas, and governance constraints — and cuts the safe global capacity to about 1,460 gigatons. That’s roughly 90 percent lower than industry and model assumptions. The implications are stark. If storage is used to offset ongoing fossil fuel use, there isn’t enough left to handle removals later in the century. At best, the prudent limit could deliver 0.7 °C of cooling if reserved entirely for durable removals. Many 2 °C and even 1.5 °C scenarios already exceed this budget. That means CCS cannot serve as both fossil lifeline and overshoot fix. Its role should be tightly focused: hard-to-abate industrial sectors, relatively pure biogenic CO₂, and only limited amounts of direct air capture. Using storage to prolong coal and gas makes no sense when every ton stored reduces the future cooling potential. Storage is scarce, risky, unevenly distributed, and expensive. Policymakers should treat it as a finite global commons, not a backstop. The real path to climate stabilization remains what it always was: rapid emissions reductions and electrification, with CCS reserved for the narrow niches where it truly adds value.

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Is Carbon Voice a competitor to meeting notetakers? It’s a great question that I’ve been asked a lot lately. Yes, you can use Voice Memos in Carbon Voice to record a meeting. Voice Memos are automatically transcribed, and AI can summarize, extract action items, or even answer open-ended questions. In that sense, there are similarities. But what truly sets Carbon Voice apart is that you DON'T NEED TO HAVE THE CALL OR MEETING IN THE FIRST PLACE. Here’s how to think about it: 🗓️ Meeting notetakers are for people who love meetings but hate taking notes. 🎙️ Carbon Voice is for people who love collaboration but want outcomes fast. No waiting for calls to get scheduled. No slowing down for yet another Zoom. Just moving at the speed of thought. Every conversations automatically documented, summarized, action items, and turned into action items and shared knowledge. Love meetings? Use a notetaker. Value velocity collaboration? Use Carbon Voice.

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Fantastic article and paper, but seems to be even the authors missed the bigger point. Yes, in addition to climate change impairing biodiversity, loss of biodiversity increases climate change by reducing ecosystem net carbon uptake. But by focussing on the trees (literally), seems to me they miss the bigger forest (figuratively). I hope you'lll allow me to try to write a higher level wrapper to this excellent paper... Every ecosystem is an energy system seeking to maximize how much energy it captures from the sun (ref: pretty much anything by Axel Kleidon). Until it reaches an absolute maximum, it accumulates carbon in its own biomass in diverse organisms that further help it, as a system, to capture more and even more energy. Remove or impair any or many actors in that system that are cycling the energy in captured carbon compounds, and the capacity of the system to capture energy will also likely be impaired, and thus, too, the further capture of carbon. Furthermore, as the impact of a peak-performing ecosystem is more than just carbon, but includes water cycling, radiation uptake and reflection, air pressure and movement, etc., the negative impact of lost biodiversity will be broader than on just stored chemical energy in carbon compounds, but also include other ecosystem energy dynamics. Bottom line - biodiversity isn't aesthetic, it's functional. Biodiversity isn't a "nice to have", it's foundational in how the earth energy system works in its process of seeking maximum solar energy capture. https://lnkd.in/gTCFw3Vc

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I was honored to share GaiaVerse Ltd.’s GaiaGraph at the recent AI for Good keynote session on Agentic AI for regenerative and socially just planetary stewardship. It’s rare to see a framework built not just for progress, but for healing. ❤️‍🩹 What GaiaGraph brings: A decision-intelligence system that embraces complexity and uncertainty — drawing on relational insights rather than enforcing control. Tools rooted in multiple knowledge systems, integrating environmental, cultural, and planetary perspectives. A way to highlight narrative shifts and hidden interdependencies that often get overlooked in traditional decision models. Alignment with relational values, justice, humility — especially for communities who have historically been left out of decision-making. Why this matters: Because climate and social justice challenges are intertwined. We need tools that reflect that—capable of seeing the bigger picture. Because moving from extractive models toward regenerative ones changes who benefits, how decisions are made, and what is considered “success.” Because when AI is grounded in care, coherence, and transparency, it can become more than a driver of efficiency—it can become a force for restoring balance. If you’re working at the intersection of AI, planetary health, or community-led innovation, I’d love to connect and explore how we can bring frameworks like GaiaGraph into practice. Thanks to the ITU / AI for Good community for creating space for this conversation. What struck you most from my keynote presentation? I’m curious to hear your thoughts on how agentic AI can shift power, not just provide predictions. https://lnkd.in/eAgvCSWg

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