[TENNIS-TV] Denis Shapovalov vs Francis Tiafoe: Live Stream (Tennis Online 2019)
Bibliographic record
Abstract
Watch Live: https://allsports2022.blogspot.com/2019/02/tennis-live.html Watch Live: https://allsports2022.blogspot.com/2019/02/tennis-live.html Canadian tennis superstar, Denis Shapovalov has made it into the quarterfinal of the 2019 ATP Miami Open. At this stage of the tournament, Shapovalov will play against Frances Tiafoe from the United States. Frances Tiafoe had a big win in the previous round as he eliminated David Goffin by beating him in straight sets. It was a close match but Tiafoe excelled with his returning which gave him a massive edge. During the win, Frances Tiafoe won 42% of his return points which allowed him to break David Goffin’s serve on 4 occasions. Tiafoe won 75% of his service games and a second set tiebreaker to seal the outcome in his favour. Frances Tiafoe has received incredible support during this win as he is playing in his home country. On the other side of the coin, Denis Shapovalov also had a big win as he eliminated the number 8 seed, Stefanos Tsitsipas. It was a close match where Shapovalov lost the opening set but fought back well to eliminate one of the top players in the world. Shapovalov played well during the win as he won 88% of his service games and 2 return games. Winning against Stefanos Tsitsipas will have given Denis Shapovalov a huge boost in confidence which he should take into this match. Frances Tiafoe and Denis Shapovalov have played twice in the past with both having won on one occasion. The most recent match between these two players was in 2018 at the Cincinnati Masters where Denis Shapovalov won in 3 sets. Frances Tiafoe’s win over Shapovalov was also in 2018 at the Delray Beach Open. As far as this match is concerned, I am picking Frances Tiafoe to win. Both players have been playing well but TIafoe has the advantage of playing at home which should give him a big
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.303 | 0.135 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".