Galtres Stakes

The Galtres Stakes is a Listed flat horse race in Great Britain open to fillies and mares aged three years or older. It is run at York over a distance of 1 mile 3 furlongs and 188 yards (2,385 metres), and it is scheduled to take place each year in August. Since 2014 the race has carried the name of Sir Henry Cecil, a former British flat racing Champion Trainer who died in 2013. It is currently held on the second day of York's four-day Ebor Festival meeting.

Galtres Stakes
Listed race
LocationYork Racecourse
York, England
Race typeFlat / Thoroughbred
SponsorBritish EBF
WebsiteYork
Race information
Distance1m 3f 188y (2,385 metres)
SurfaceTurf
TrackLeft-handed
QualificationThree-years-old and up
fillies & mares
exc. Group race winners and Group 1 placed since two years old.
Weight8 st 12 lb (3yo);
9 st 7 lb (4yo+)
Penalties
4 lb for Listed winners*
* after 2018
Purse£70,000 (2019)
1st: £39,697
Galtres Stakes
2019
Search For A Song Vivionn Spirit Of Appin

Records

Most successful horse since 1988:

  • no horse has won this race more than once

Leading jockey since 1988 (7 wins):

  • Frankie DettoriMadiriya (1990), Nibbs Point (1991), Cunning (1992), Mezzo Soprano (2003), Our Obsession (2013), Martlet (2015), Lah Ti Dar (2018)

Leading trainer since 1988 (8 wins):

  • Luca CumaniMadiriya (1990), Nibbs Point (1991), Cunning (1992), Kithanga (1993), Noble Rose (1994), Larrocha (1995), Kaliana (1997), Innuendo (1999)

Winners since 1988

Year Winner Age Jockey Trainer Time
1988 Upend 3 Steve Cauthen Henry Cecil 2:34.88
1989 Knoosh 3 Walter Swinburn Michael Stoute 2:30.77
1990 Madiriya 3 Frankie Dettori Luca Cumani 2:29.06
1991 Nibbs Point 3 Frankie Dettori Luca Cumani 2:34.86
1992 Cunning 3 Frankie Dettori Luca Cumani 2:28.57
1993 Kithanga 3 Ray Cochrane Luca Cumani 2:33.14
1994 Noble Rose 3 Michael Kinane Luca Cumani 2:29.96
1995 Larrocha 3 Michael Kinane Luca Cumani 2:28.54
1996 Eva Luna 4 Pat Eddery Henry Cecil 2:27.14
1997 Kaliana 3 John Reid Luca Cumani 2:33.76
1998 Rambling Rose 3 Pat Eddery Michael Stoute 2:25.93
1999 Innuendo 4 John Reid Luca Cumani 2:32.24
2000 Firecrest 3 Richard Quinn John Dunlop 2:28.55
2001 Inchiri 3 Franny Norton Gerard Butler 2:30.60
2002 Alexander Three D 3 Michael Hills Barry Hills 2:27.04
2003 Mezzo Soprano 3 Frankie Dettori Saeed Bin Suroor 2:28.85
2004 Tarakala 3 Michael Kinane John Oxx 2:37.68
2005 Kastoria 4 Michael Kinane John Oxx 2:30.36
2006 Anna Pavlova 3 Paul Hanagan Richard Fahey 2:36.60
2007 Wannabe Posh 4 Eddie Ahern John Dunlop 2:33.06
2008 no race 2008 [lower-alpha 1]
2009 Tanoura 3 Michael Kinane John Oxx 2:32.64
2010 Brushing 4 Kieren Fallon Mark Tompkins 2:31.97
2011 Set To Music 3 Jamie Spencer Michael Bell 2:35.94
2012 Pale Mimosa 3 Pat Smullen Dermot Weld 2:31.98
2013 Our Obsession 3 Frankie Dettori William Haggas 2:31.99
2014 Queen of Ice 3 Andrea Atzeni William Haggas 2:32.42
2015 Martlet 3 Frankie Dettori John Gosden 2:32.35
2016 Abingdon 3 Andrea Atzeni Sir Michael Stoute 2:33.84
2017 Fleur Forsyte 3 Daniel Muscutt James Fanshawe 2:37.12
2018 Lah Ti Dar 3 Frankie Dettori John Gosden 2:31.22
2019 Search For A Song 3 Oisin Murphy Dermot Weld 2:30.30
  1. The 2008 running was abandoned because of a waterlogged course
gollark: Fearsome.
gollark: I might have to release apioforms from the beecloud.
gollark: It must comfort you to think so.
gollark: > There is burgeoning interest in designing AI-basedsystems to assist humans in designing computing systems,including tools that automatically generate computer code.The most notable of these comes in the form of the first self-described ‘AI pair programmer’, GitHub Copilot, a languagemodel trained over open-source GitHub code. However, codeoften contains bugs—and so, given the vast quantity of unvettedcode that Copilot has processed, it is certain that the languagemodel will have learned from exploitable, buggy code. Thisraises concerns on the security of Copilot’s code contributions.In this work, we systematically investigate the prevalence andconditions that can cause GitHub Copilot to recommend insecurecode. To perform this analysis we prompt Copilot to generatecode in scenarios relevant to high-risk CWEs (e.g. those fromMITRE’s “Top 25” list). We explore Copilot’s performance onthree distinct code generation axes—examining how it performsgiven diversity of weaknesses, diversity of prompts, and diversityof domains. In total, we produce 89 different scenarios forCopilot to complete, producing 1,692 programs. Of these, wefound approximately 40 % to be vulnerable.Index Terms—Cybersecurity, AI, code generation, CWE
gollark: https://arxiv.org/pdf/2108.09293.pdf

See also

References

  • Paris-Turf:
    • "1983"., "1984"., "1986".
  • Racing Post:
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.