1994 Sacramento Gold Miners season
The 1994 Sacramento Gold Miners season was the second for the team in the Canadian Football League. The team finished in 5th place in the West Division with a 9–8–1 record and failed to make the playoffs.
| 1994 Sacramento Gold Miners season | |
|---|---|
| Head coach | Kay Stephenson |
| Home field | Hornet Stadium |
| Results | |
| Record | 9–8–1 |
| Division place | 5th, West |
| Playoff finish | did not qualify |
| Uniform | |
 | |
Pre-season
- Vs. Calgary L 39–24 (13,650)
- At Sask L 19–4 (26,850)
Regular season
Standings
| Team | GP | W | L | T | PF | PA | Pts |
|---|---|---|---|---|---|---|---|
| Calgary Stampeders | 18 | 15 | 3 | 0 | 698 | 355 | 30 |
| Edmonton Eskimos | 18 | 13 | 5 | 0 | 518 | 401 | 26 |
| BC Lions | 18 | 11 | 6 | 1 | 604 | 456 | 23 |
| Saskatchewan Roughriders | 18 | 11 | 7 | 0 | 512 | 454 | 22 |
| Sacramento Gold Miners | 18 | 9 | 8 | 1 | 436 | 436 | 19 |
| Las Vegas Posse | 18 | 5 | 13 | 0 | 447 | 622 | 10 |
Regular Season
- Vs. Las Vegas L 32–26 (14,816)
- At Hamilton W 25–22 (19,291)
- At Las Vegas W 22–20 (10,740)
- Vs. Sask W 30–27 (14,828)
- At BC L 46–10 (18,459)
- At Calgary L 25–11 (21,110)
- Vs. Edmonton L 44–15 (13,959)
- At Winnipeg L 31–28 (21,804)
- Vs. BC T 15–15 (12,633)
- At Baltimore W 30–29 (42,116)
- Vs. Shreveport W 56–3 (13,741)
- Vs. Calgary L 39–25 (17,192)
- At Sask W 19–16 (23,669)
- Vs. Toronto W 34–32 (13,050)
- At Shreveport L 24–12 (12,465)
- Vs. Ottawa W 44–9 (13,760)
- At Edmonton L 22–16 (29,332)
- Vs. Baltimore W 18–0 (14,056)
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
References
- "Archived copy". Archived from the original on 2012-09-22. Retrieved 2013-11-29.CS1 maint: archived copy as title (link)
- http://www.profootballarchives.com/1994cflsac.html
External links
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