Good explanation on fitting competition models in forest genetic trials using asreml:
Competitive Genetics: Exploring the impact of direct and indirect genetic effects in tree breeding. https://vsni.co.uk/blogs/competitive-genetics
Based on the work by Joao Costa e Silva and Richard Kerr “Accounting for competition in genetic analysis, with particular emphasis on forest genetic trials” https://link.springer.com/article/10.1007/s11295-012-0521-8
I’m often fascinated by the lengths people will go to get something they like, as in “Reaching Fuller Flavor Profiles with the AeroPress” https://coffeeadastra.com/2021/09/07/reaching-fuller-flavor-profiles-with-the-aeropress/
I am coding simulations where I call asreml() and it was taking longer than necessary, because it was sending the convergence information to the screen. The trick to get rid of that is to use the trace = FALSE option as in
asreml(trait_1 ~ 1, random = ~ mum, data = current_trial, trace = FALSE)Sometimes I am testing graphs and I want to get rid of the lines (coef = 0) and outliers (outlier.shape = NA), particularly if I’m overlapping points.
geom_boxplot(coef = 0, outlier.shape = NA)Danielle Navarro wrote a three-post series introducing Julia almost from scratch. Fantastic work. Bookmarking.
By Bruce Weir and Jerome Goudet. I added it to the Internet Archive as well. https://www2.unil.ch/popgen/teaching/SISG23/
Would data augmentation improve the performance of within-family selection in forestry datasets?
Data Augmentation Enhances Plant-Genomic-Enabled Predictions https://www.mdpi.com/2073-4425/15/3/286
Looking for alternatives to the lidR R package, for research purposes, plus some resources to start working on it.
New collection of Jupyter Notebooks enables programmatic access to cloud-hosted USGS 3D Elevation Program (3DEP) lidar data
https://opentopography.org/blog/new-collection-jupyter-notebooks-enables-programmatic-access-cloud-hosted-usgs-3d-elevation
Big Data Geospatial Processing for Massive Aerial LiDAR Datasets https://www.mdpi.com/2072-4292/12/4/719 uses Spark
A guide covering LiDAR including the applications, libraries and tools that will make you better and more efficient with LiDAR development.
https://github.com/mikeroyal/LiDAR-Guide
Learning from LiDAR Data with Deep Learning https://www.strong.io/blog/deep-learning-for-lidar-data
Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes https://doi.org/10.1016/j.compag.2020.105815
“As we may think” by Vannevar Bush (1945), the introduction of the Memex (local copy PDF)
https://web.mit.edu/STS.035/www/PDFs/think.pdf
“You and Your Research” by Richard Hamming (1986), on developing research priorities
https://www.cs.virginia.edu/~robins/YouAndYourResearch.html
Tim Bray making the case that there is an overly optimistic understanding or, another way, a general misunderstanding of LLM that will suck a huge amount of value from financial markets.
https://www.tbray.org/ongoing/When/202x/2024/02/25/Money-AI-Bubble
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