Tootfinder

Since it was relevant to a discussion I just had on here and is something most people probably haven't thought about much (unless you've taken one of a handful of philosophy classes), I thought I'd try to lay out a key piece of Descartes' Meditations (#philosophy

From bluesky, the Biodiversity Heritage Library€ who I ❤️ ❤️ ❤️:
‪@biodivlibrary.bsky.social€ BHL is featured in
@theguardian.com's Age of Extinction series.
The article highlights the extraordinary value of BHL and the urgent need to ensure it remains free and open into the future.
Please read and share:

"This week, a report from Royal Botanic Gardens (RBG), Kew revealed the crucial role digitisation is playing in “transforming our ability to understand and respond to the climate and biodiversity crises”, but it was the creation of the BHL 20 years ago that first demonstrated how bringing centuries of scientific knowledge online can unlock transformative discoveries and insights about the natural world." (

History of #Minnesota: forests, fires, policy
"The Past, Present, and Future of Forest Fires" by Hayden L. Nelson
https://niche-canada.org/2026/06/13/th

The Past, Present, and Future of Forest Fires
Upfront costs–in research, in personnel, and in deployment–are essential for fire prevention. Yet today, as in the early twentieth century, the federal government, despite possessing knowledge indicating the opposite, would rather spend money in disaster relief than in disaster prevention.

A Scoping Review of Physics Informed Machine Learning for Wave Propagation Modeling in Seismology
\'Oscar Rinc\'on-Carde\~no, Gregorio P\'erez-Bernal, Silvana Montoya-Noguera, Nicol\'as Guar\'in-Zapata
https://arxiv.org/abs/2607.00178 https://arxiv.org/pdf/2607.00178 https://arxiv.org/html/2607.00178
arXiv:2607.00178v1 Announce Type: new
Abstract: \emph{Background:} Standard numerical methods accurately simulate seismic waves but are computationally expensive, particularly for inverse problems. Machine learning approaches have been proposed as alternatives that can reduce computational cost while maintaining acceptable physical accuracy. \emph{Objective:} To map how physics-informed machine learning methods have been applied to seismic wave propagation modeling based on partial differential equations. \emph{Methods:} A scoping review was conducted using the OpenAlex and Scopus databases. Selected studies were classified by problem type (forward or inverse) and machine learning strategy to identify research trends, methodological patterns, and gaps in the literature. \emph{Results:} Physics-informed machine learning has been applied to both forward modeling and inversion in seismology, often reaching accuracy comparable to standard numerical methods at lower computational cost. Application of three mechanisms for incorporating physical knowledge were identified: observational bias, inductive bias, and learning bias. To evaluate methodological reproducibility of a representative method, the original PINN framework was replicated in PyTorch, obtaining results consistent with and in most cases more accurate than those originally reported. From the reviewed literature, limitations remain in benchmarking consistency, training cost, and scalability to three-dimensional and experimentally validated problems. \emph{Conclusions:} Standard numerical methods remain the basis of seismological workflows, while physics-informed machine learning offers complementary approaches that are useful for inverse problems and surrogate modeling. Future work should focus on consistent benchmarking, hybrid formulations, and validation under realistic geophysical conditions.
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