I don't code much anymore, the vast majority is reviews the only time I really need to get on the tools is if it's a concept that I need to teach someone, or more likely, something has gone quite badly wrong and it's 2100 and I don't feel like waking anyone.
Very good work - but typically we don't build prospectivity models this way (or rather we don't validate them this way anymore). Great to see the USGS starting to dip their toe back in this though, they and the GSC were long the leaders in this, but have dropped it on the last 5-7 years.
I like KoBold a lot, but this is a wild take on what has happened so far - a traditional mining exploration technique found this deposit, KoBold then brought into this as a typical cashed up midtier/large miner would and haven't released a resource and reserve calculation at all.
Welcome to the world of resource and reserve calculations, where the numbers are made up and the points don't matter.
It's very easy to come up with a back of the envelope calculation to say we have this much Lithium, or this much Copper and come up with an absolute whopper of a number, the question is how much of it is recoverable and can it form part of a resource. There's a reason we (mining industry) don't let people produce these kind of numbers offhand, it's wildly speculatory and holds no basis in mining reality (yet!) - however very cool stuff and might provide a new exploration target in the future.
Oh man - there's actually a much cooler version of this scenario, which is Sudbury impact crater, which was a massive Nickel deposit that was 'enriched' by the impact crater that hit it, not by adding Nickel, but by adding heat into the system.
I think it's worth reading the article mate, it's talking about how they observed that satellite emissions and the effect it could have on their research and the things they do.
I think is true for >general< data science houses and firms that offer Data Science across any domain - I'm generally wary of anyone who writes about Data Science as a concept, rather than the use of Data Science to solve a particular problem in a particular domain, the rise of the Machine Learning bros is very real.
The major problem for copper is the lack of large projects in the pipeline - current mineral explorers are entering a new phase of what they need to look for and it's a super exciting time to be working in Exploration, honestly.
P.S - Incredibly biased as I work in Mineral Exploration.
It's more to do with the overall intensity of the anomaly. If you'd like to become thoroughly confused, I'd recommend looking up remnant magnetisation, which is the bane of my existence.
I'm not sure I'm quite following your train of thought, is the implication that offshore wind farms are going to have a greater negative effect on the planet than our current energy mix? I don't think anyone who works in this field from a technical level has an opinion that you can plonk down a solar panel farm and have a net zero effect on the local environment, but there's a bunch of examples of where there's been an accidental positive feedback loop that's occurred with solar panels in certain regions. This is specifically not cited by the article.
Apologies for the lack of clarity in that sentence, there is a recognition that intermittence is one of the biggest challenges that is currently faced by the power grid in it's current form. But we actually have ways to get around this currently, it's just very rare for them to be put in place (there's also some large omissions from the current state of the South Australian power grid that is mentioned).
We still have to explore for coal as an industry, too. So should we presume that the moment our current coal reserves run out that this is the end of all coal power?
No.
Anyways, no-one has really made a case for renewables being a silver-bullet who works in energy policy, I would not be inclined to use this article as a touchstone for the problems the industry faces.