The Score
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The Score
Their target is a royal sceptre smuggled into Canada but discovered by customs, now stored in the ultra-secure basement of the Montréal Customs House. Jack has infiltrated the Customs House by posing as a mentally-challenged janitor, and Nick finds access to the basement through the sewers beneath. Diane, disappointed that Nick has taken on this final score, reconsiders their future together.
On Rotten Tomatoes the film has a "Certified Fresh" 73% rating based on 128 reviews, with an average rating of 6.5/10. The website's critical consensus reads, "Though the movie treads familiar ground in the heist/caper genre, De Niro, Norton, and Brando make the movie worth watching."[15] On Metacritic the film has a score of 71% based on reviews from 29 critics.[16]
In general, only metrics contribute to your Lighthouse Performance score, not the results of Opportunities or Diagnostics. That said, improving the opportunities and diagnostics likely improve the metric values, so there is an indirect relationship.
A lot of the variability in your overall Performance score and metric values is not due to Lighthouse. When your Performance score fluctuates it's usually because of changes in underlying conditions. Common problems include:
Furthermore, even though Lighthouse can provide you a single overall Performance score, it might be more useful to think of your site performance as a distribution of scores, rather than a single number. See the introduction of User-Centric Performance Metrics to understand why.
The Performance score is a weighted average of the metric scores. Naturally, more heavily weighted metrics have a bigger effect on your overall Performance score. The metric scores are not visible in the report, but are calculated under the hood.
Once Lighthouse has gathered the performance metrics (mostly reported in milliseconds), it converts each raw metric value into a metric score from 0 to 100 by looking where the metric value falls on its Lighthouse scoring distribution. The scoring distribution is a log-normal distribution derived from the performance metrics of real website performance data on HTTP Archive.
For example, Largest Contentful Paint (LCP) measures when a user perceives that the largest content of a page is visible. The metric value for LCP represents the time duration between the user initiating the page load and the page rendering its primary content. Based on real website data, top-performing sites render LCP in about 1,220ms, so that metric value is mapped to a score of 99.
Going a bit deeper, the Lighthouse scoring curve model uses HTTPArchive data to determine two control points that then set the shape of a log-normal curve. The 25th percentile of HTTPArchive data becomes a score of 50 (the median control point), and the 8th percentile becomes a score of 90 (the good/green control point). While exploring the scoring curve plot below, note that between 0.50 and 0.92, there's a near-linear relationship between metric value and score. Around a score of 0.96 is the "point of diminishing returns" as above it, the curve pulls away, requiring increasingly more metric improvement to improve an already high score.
As mentioned above, the score curves are determined from real performance data. Prior to Lighthouse v6, all score curves were based on mobile performance data, however a desktop Lighthouse run would use that. In practice, this led to artificially inflated desktop scores. Lighthouse v6 fixed this bug by using specific desktop scoring. While you certainly can expect overall changes in your perf score from 5 to 6, any scores for desktop will be significantly different.
To provide a good user experience, sites should strive to have a good score (90-100). A "perfect" score of 100 is extremely challenging to achieve and not expected. For example, taking a score from 99 to 100 needs about the same amount of metric improvement that would take a 90 to 94.
CHANG: But what if the story is also funny and absurd and even a little tongue-in-cheek Well, that was the task for composer Nathan Johnson, who wrote the score for the film "Glass Onion: A Knives Out Mystery." Johnson sat down with Robin Hilton from NPR's All Songs Considered podcast to peel back the layers of the soundtrack, starting with the main theme.
JOHNSON: Yeah. So with "Lights Out," you know, this was kind of this part of the score where we dip into more of that ambient world. So these are - a lot of the percussion is happening on the string instruments. One of the sounds is a very amplified rubber ball dragging across the head of a drum. There's also these spider-like sounds, which is all the string players tapping the wooden parts of their bows on the strings col legno, but not together. So we're having them do these kind of cascading runs that are very purposefully not in line with each other. And that's a really fun thing when you get to sit down with the players and say, show me what you can do with your instrument that might surprise me.
Burden tests take prioritization to the next level by aggregating the variants observed at a given locus to calculate a burden score for the gene. Most burden testing software tools also evaluate potentially damaging genotypes in the context of other genotypes observed at the same locus in a control population.
A gene prioritization approach that scores, ranks and prioritizes genes based on genotypes rather than on single variants. The observed (or for some methods, the theoretical) distribution of burden scores within the wider population is often used to rank a proband's genotype score. Many burden tests can also incorporate adjunct information into their calculations such as phylogenetic conservation, mode of inheritance and variant frequency data. Unlike variant prioritization tools, burden tests require access to genotype data for their calculations.
The score band indicates a range of scores, including scores slightly higher and slightly lower than the score received. The test taker's actual proficiency in the skills measured is likely to fall within this range. As an example, a score of 157 would be reported along with a score band of 154 to 160.
The LSAT, like any standardized test, is not a perfect measuring instrument. One way to quantify the amount of measurement error associated with LSAT scores is through the calculation of the standard error of measurement. The standard error of measurement provides an estimate of the average error that is present in test scores because of the imperfect nature of the test.
The standard error of measurement for the LSAT is very stable, and tends to be about 2.6 scaled score points. A score band with a 68 percent confidence level can be constructed by subtracting the standard error of measurement from the scaled score to obtain the lower value and adding the standard error of measurement to the scaled score to obtain the upper value. Therefore, the width of the score band is approximately 7 scaled-score points, after rounding.
The 68 percent (or approximately two out of three) level of confidence used by LSAC for reporting purposes is a commonly used standard. To obtain a 95 percent level of confidence, the standard error of measurement can be doubled before constructing the score band. Therefore, a 95 percent confidence band would be approximately twice as wide as a 68 percent confidence band.
For test takers who take the test more than once, the standard error of measurement is calculated in a similar way as that described above. However, there is less measurement error associated with an average score than there is with a score earned on a single day of testing. The standard error of measurement is adjusted to take into account the number of scores earned by the candidate in calculating the score band for an average score, resulting in a somewhat narrower band.
The main body of the DVD presents Bilson in a 90-minute lecture before a live audience examining aspects of notation of Mozart, Beethoven, Chopin, Prokofiev, Schubert, and Bartók, showing clearly that there is far more expressive information in these scores than is usually presumed. 59ce067264