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op_ppl = (40M to 120M) / (70k to 200k) sff_ppl = (40M to 60M) / (70k to 200k) fli_ppl = (1M to 30M) / (50k to 150k) ltff_ppl = (3M to 7M) / (50k to 150k) longview_ppl = (2M to 15M) / (50k to 150k) manifund_ppl = (1M to 3M) / (30k to 200k) small_funders_ppl = (5M to 40M) / (60k to 200k) individual_funders_ppl = (5M to 40M) / (60k to 100k) nsf_ppl = 5M / (40k to 150k) dsit_ppl = (2M to 10.8M) / (40k to 100k)

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million = 10^6 billion = 10^9 a = 250 * million to 25 * billion b = 200 * million to 7.5 * billion c = 2 to 4 d = 1 to 4 benefit = (a+b)*c*d

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/* Modeling the probability and severity of volcanic cooling events Like my other model, but uses geomean of odds to calculate final probabilities, rather than geomean of probabilities */ // Method 1: Based on the assumption that (A) 1+ degree cooling events happen with a period of 150 years, (B) 1.5+ degree cooling events happen with a period of 500 years, and (C) 2+ degree cooling events happen with a period of 3000 years [Source: Fig. 1d from Stoffel et al. (2015): https://dendrolab.ch/wp-content/uploads/2018/10/Stoffel_etal_NGEO_2015.pdf] // Construct four models, two exponential and two power-law, and use a mixture of all four models. // Incorporate uncertainty into the relative frequency of 1, 1.5 and 2-degree cooling events. 1.5-degree cooling events represent approximately 30% of 1-degree cooling events, and 2-degree cooling events represent approximately 5% of 1-degree cooling events. I intruduce a subjective amount of uncertainty to these figures such that the 95th percentile is approximately 2x the mean:

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/* Modeling the probability and severity of volcanic cooling events */ // Method 1: Based on the assumption that (A) 1+ degree cooling events happen with a period of 150 years, (B) 1.5+ degree cooling events happen with a period of 500 years, and (C) 2+ degree cooling events happen with a period of 3000 years [Source: Fig. 1d from Stoffel et al. (2015): https://dendrolab.ch/wp-content/uploads/2018/10/Stoffel_etal_NGEO_2015.pdf] // Construct four models, two exponential and two power-law, and use a mixture of all four models. // Incorporate uncertainty into the relative frequency of 1, 1.5 and 2-degree cooling events. 1.5-degree cooling events represent approximately 30% of 1-degree cooling events, and 2-degree cooling events represent approximately 5% of 1-degree cooling events. I intruduce a subjective amount of uncertainty to these figures such that the 95th percentile is approximately 2x the mean:

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/* How much time did I spend on my PhD? several ways */ year_hours_estimate = { t2019 = 300 to 500 // only three months t2020 = 800 to 1600 t2021 = 400 to 1000 t2022 = 200 to 600 t2023 = 250 to 300

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/* Modeling the global cooling rate expected from a large-scale nuclear exchange */ // Using the competing Reisner and Toon claims on the soot injection of a regional nuclear exchange as a basis, this model extrapolates to estimate the level of cooling expected from a nuclear war of at least 100 detonations. // Black carbon (known as BC or soot) in stratosphere from 100x15kT-warhead nuclear exchange (Tg), all strikes countervalue. Use the Toon estimate of 5Tg as the 95th percentile. Reisner estimates 0.2Tg, but claims this is an overestimate since they assume all combustible material is converted to BC, while the true amount would be 10-100 times less. Denkenberger & Pearce (2018) models the soot-emission factor as lognormally-distributed with 90% CI (1%,4%), which has mean value 2.2%. Hence our lower estimate, which we use as the 5th percentile, is 0.2Tg * 0.022 = 0.0044Tg. Use a lognormal distribution with these 5th & 95th percentiles, and replace the top and bottom 5% with constants 0.0044 and 5, to avoid unreasonably high outlier values. bc_min_all_countervalue = mx(truncate(0.0044 to 5,0.0044,5),0.0044,5,[0.9,0.05,0.05])