Bibliographic record
Abstract
Ingo is a Guest Coeditor of this special issue on Aquatic Models for Biomedical Evo-Devo. Website: https://www.fishevodevogeno.org/ Google scholar page: https://scholar.google.com/citations?user=xVw8dCAAAAAJ I studied biology at the University of Konstanz, Germany, and worked as an undergraduate on my first comparative fish genomics projects in the group of Axel Meyer with two fantastic postdocs at the time: John S. Taylor, now faculty at the University of Victoria, Canada, and Walter Salzburger, now faculty at the University of Basel, Switzerland. For my doctoral work, I joined Manfred Schartl and Jean-Nicolas Volff at the University of Würzburg, also in Germany, studying the functional genetic impacts of whole genome duplications on the evolution of vertebrate pigmentation. For my postdoc, I worked in the group of John H. Postlethwait at the University of Oregon in Eugene. John's group had just started to use spotted gar as a genomic outgroup to the teleost fishes and the teleost genome duplication. There, I began developing spotted gar as a developmental and functional genomic model system for vertebrate biology and EvoDevo – work that continues in my laboratory at Michigan State University. I grew up in provincial Germany as the son of a high school chemistry and physics teacher and a pharmaceutical technician, so I was exposed to the natural sciences early on. Starting in elementary school, I developed a passion for reading about dinosaurs and prehistoric people, years before Jurassic Park made paleontology cool. Thus, although I didn't know the term then, I had an early appreciation for macroevolution. In high school, I kept all kinds of aquarium fishes (can you ever have too many tanks?), while reading about Darwin's Voyage of the Beagle, evolution, and genetics. This fascination kept going and was a reason I chose the University of Konstanz for undergraduate studies because of its strong curricular focus on molecular biology. Working as an undergraduate researcher in the Meyer Lab and being surrounded by an international crew of world-class molecular evolutionary biologists around me – who even used fish models to answer big questions about the deep evolutionary history of vertebrates – was immensely thrilling. Comparing sequences from diverse organisms and reconstructing their evolutionary change across phylogenies, I could practically look back in time! I knew I had found my path. However, sequencing DNA and analyzing genetic information on the computer was not enough for me. Fondly remembering my childhood fish breeding projects and the beauty of watching fish embryos grow, I successively added developmental biology to my research portfolio. The name of my research group, the Fish Evo Devo Geno Lab, reflects this multipronged approach. Observing the elegance of developmental processes in many different fish species is my happy place. How could anyone ever just want to look at one research organism? Over my research career, I have worked with zebrafish, medaka, cichlids, platyfish and swordtails, killifishes, gars, bowfin, and many others, not to mention all the fish genomes we have analyzed in addition. To me, this is at the core of EvoDevo research – to be able to appreciate, work with, and sometimes unravel some mechanistic underpinnings of the “endless forms most beautiful.” And at the same time, since no single lab can keep all the model organisms or be experts in all necessary methods, comparative EvoDevo research is inherently collaborative, and, I strongly think, also particularly open-minded and mind-opening. There are clearly great times ahead of us with the incredible progress in genomics, genome editing, transgenesis, in vivo imaging, and computational advances including artificial intelligence. Mountains of correlative data need to be functionally tested in diverse research organisms to make actual causal links between genotype and phenotype – and naturally EvoDevo research will lead the charge. Because of the interdisciplinary nature of our field, you might find yourself frequently in situations – be it in graduate school, at conferences, or in the department you newly joined as faculty – in which your way of thinking, your ideas, and your research is considered outside the mainstream of any of the more specialized disciplines we aim to integrate. Make your EvoDevo research attractive to both basic research as well as to more applied and biomedical funding mechanisms. Stay confident, embrace the big picture, and trust in your abilities to see beyond the intellectual silos and blinders of individual research fields. Fortunately, with the formation of the Pan-American Society for Evolutionary Developmental Biology and the European Society for Evolutionary Developmental Biology over the past 20 years that I personally consider my intellectual homes, we now have plenty of opportunities to network within our buzzing community and jointly advocate for the EvoDevo mindset. Come join us!
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".