2003 China Open – Doubles
Anna Kournikova and Janet Lee were the defending champions, but Kournikova did not compete this year. Lee teamed up with Corina Morariu and lost in quarterfinals to Ai Sugiyama and Tamarine Tanasugarn.
Doubles | |
---|---|
2003 China Open | |
Champions | |
Runners-up | |
Final score | 6–3, 6–3 |
Émilie Loit and Nicole Pratt won the title by defeating Ai Sugiyama and Tamarine Tanasugarn 6–3, 6–3 in the final.
Seeds
Conchita Martínez / Angelique Widjaja (Semifinals) Cara Black / Alicia Molik (First round) Petra Mandula / Barbara Schett (First round) Emmanuelle Gagliardi / Chanda Rubin (Semifinals)
Draws
Key
- Q = Qualifier
- WC = Wild Card
- LL = Lucky Loser
- Alt = Alternate
- SE = Special Exempt
- PR = Protected Ranking
- ITF = ITF entry
- JE = Junior Exempt
- w/o = Walkover
- r = Retired
- d = Defaulted
Draw
First Round | Quarterfinals | Semifinals | Final | ||||||||||||||||||||||||
1 | 7 | 6 | |||||||||||||||||||||||||
5 | 3 | 1 | 6 | 6 | |||||||||||||||||||||||
6 | 6 | 2 | 4 | ||||||||||||||||||||||||
LL | 3 | 2 | 1 | 2 | 3 | ||||||||||||||||||||||
3 | 64 | 710 | 3 | 6 | 6 | ||||||||||||||||||||||
77 | 68 | 6 | 0r | ||||||||||||||||||||||||
7 | 6 | 5 | |||||||||||||||||||||||||
Q | 5 | 2 | 3 | 3 | |||||||||||||||||||||||
3 | 1 | 6 | 6 | ||||||||||||||||||||||||
6 | 6 | 0 | 2 | ||||||||||||||||||||||||
4 | 4 | 4 | 6 | 6 | |||||||||||||||||||||||
4 | 6 | 6 | 4 | 6 | 4 | 3 | |||||||||||||||||||||
5 | 6 | 6 | 3 | 6 | 6 | ||||||||||||||||||||||
7 | 3 | 1 | 6 | 6 | |||||||||||||||||||||||
78 | 3 | 6 | 4 | 3 | |||||||||||||||||||||||
2 | 66 | 6 | 3 |
gollark: All are to, broadly speaking.
gollark: Good!
gollark: FEAR the webring².
gollark: I'm sure Google has lots of spare GPU/TPU power. They have some ridiculous GPT-3-scale image/text model in development now, and use BERT-like entities for search parsing.
gollark: I'd think that it would be possible to detect it if you had a lot of samples of it versus real human text. And there was this demo highlighting differences between human and GPTous text, via highlighting low-probability-from-the-model words (which are often also the most important).
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.