At the same time, consumers preempt their future inertia; 24%-36% of potential subscribers avoid subscribing when offered an auto-renewal promo. Further, offering an auto-renewal contract decreases the share of subscribers over the two years after the promo by 10%. Even though auto-renewal generates higher revenue in the medium-run due to payments from inert subscribers, auto-renewal and auto-cancel are revenue equivalent after one year, but with fewer subscribers in auto-renewal.
I opened this article hoping it would mention the point about the rules concerning reduced sulfur content of the fuel used in super tanker marine shipping, and it didn't disappoint:
"A lot of people have looked at the impact of the marine shipping regulation change. If you take that and you put it into some climate model and you estimate the temperature change, right now you’d expect about 0.05 of a degree, 0.08 of a degree [of warming per year], and then building over a decade to about 0.1 degree. So that seems like it helps, but it doesn’t seem like it’s sufficient."
Seems like the models have quite a few holes. It made me wonder if anyone has considered making a complete list of assumptions that are baked into these models, so they can be looked at in detail.
There are families of models that focus on different things, each family with a number of members that have differing paremeter weightingss, etc.
Yes, there are big overviews of the models and how they differ - the IPCC look at the over|under predictions of all the models and look at the spread and assumptions to select a "most probable" middle ground prediction for climate going forward.
For example while the current year has been warmer than expected it's also been cooler than a number of worst case scenarios that assume faster methane releases and water vapor increases, etc.
I had a goto link for a good overview .. currently it's redirecting to:
The Lawrence Livermore National Laboratory is working on our digital Special Collections and the connection with OSTI. This includes all LLNL produced Technical Reports, Theses & Dissertations, and eSholarship content. We are working at making these available through OSTI. We apologize for the interruption in service.
> It made me wonder if anyone has considered making a complete list of assumptions that are baked into these models, so they can be looked at in detail.
Yes, they did, it's called an "ensemble model" when multiple models are collated to account for their different modelings.
A friend of mine did his physics PhD on cloud formation at a molecular level exactly to tackle the issue some models had to account for that over longer time scales, most of the holes you can think of from the top of your head have been considered, there are many thousands of very smart people working on these models for the past 30-40 years.
That doesn't surprise me. Yes, would be interesting to see those assumptions, but I guess the issue (as in most modelling of complex systems) is that as you relax the assumptions, the models become intractable.
Do you mean "a full model so that you can analyze it with fluid dynamics, differential equations, and thermodynamics", or do you mean "a pre made Gish Gallop that you can rattle off without having to actually think about any of them"?
The "models are wrong" climate change deniers basically mean that the prior likelihood of someone on the internet being genuinely interested in understanding and improving the models is below 1%.
Based on the results of their modeling approach, cycling and high intensity interval training (HIIT) produced the most consistent effects in improvement of memory, attention, executive function, information processing and other cognitive functions.
"We found that vigorous activities had the largest effects," Giesbrecht said. "Also, the effects were strongest for studies that tested cognition after exercise, as opposed to during exercise. And lastly, the effects of exercise less than 30 minutes in duration were bigger than those that went beyond 30 minutes."
"Also among their findings, the team discovered that executive functioning was the key cognitive domain impacted by vigorous exercise, such as HIIT protocols."
It's probably true that not a lot of people make constructive use of their sleep data, but people just love learning about themselves: it's kind of a form of narcissism, unless you are experimenting with methods to shift the numbers some way. For my part, I use it for a lot of statistical tests.
Sometimes I like to think of GDP as a measure of economic (productive) power more than well-being. But other indicators are more important for the long-term, such as those on demographics, education, debt levels, etc. You probably don't want to sacrifice those for a transient boost in GDP.
On average, sleep/wake classification accuracies were reported to be 87.2% based on 53 assessed devices. There was no significant difference in accuracies between devices using only accelerometer data (86.7%, d = 28) and devices using both PPG and accelerometer data (87.8%, d = 22), as determined by a t-test (significance threshold p < 0.05). All reported accuracies ranged from 79% to 96%, except for Kanady et al.’s study28, which reported lower values of 54% and 64%. This difference can be attributed to their 24-hour measurement, which had a higher wake-to-sleep ratio compared to overnight measurements in other studies. Therefore, these accuracies reflect the generally poor performance of sleep classifiers in detecting wake. The average accuracy for 3-stage classification (wake vs. NREM vs. REM) was 69.7% (d = 3), and for 4-stage classification (wake vs. light vs. deep vs. REM), it was 65.2% (d = 9).
