Task: Wrong Candidate Generation
Input (English): If the reliability test is not met and the methods or techniques are deemed unreliable, any expert testimony based upon such techniques is also considered unreliable and therefore inadmissible. See Franklin, 1999 UT 61 at ¶ 18, 987 P.2d 22. ¶ 22 Thus, the question before us is whether canine accelerant detection is novel scientific evidence. Scientific methods or tech- ñiques are not considered novel if they are “of the sort that ha[s] ‘attained general acceptance in ... the relevant scientific community.’ ” Patey v. Lainhart, 1999 UT 31, ¶ 16, 977 P.2d 1193 (ellipsis in original) (citation omitted). If scientific testimony is not based on novel scientific principles or techniques, the Rimmasch test is not implicated. See id.; see also State v. Adams, 2000 UT 42, ¶ 16, 5 P.3d 642 (<HOLDING>). Instead, the testimony must simply comply 
 Holding statements: (A) holding that daubert applies not only to testimony based on scientific knowledge but also to testimony based on technical and other specialized knowledge (B) holding seventh state petition for postconviction relief which was based on newly discovered evidence but rejected by the state courts because the evidence was not newly discovered was properly filed (C) recognizing under fre 702 that there is no clear line dividing testimony based on scientific knowledge from testimony based on technical or other specialized knowledge holding that a single flexible test for reliability applies to all expert testimony (D) holding that the district court did not abuse its discretion in denying motion for new trial based on newly discovered evidence where the evidence would serve only to impeach  testimony (E) recognizing that rimmasch is not intended to apply to all expert testimony but only for testimony based on newly discovered principles  quotations and citation omitted
Output (English): holding that daubert applies not only to testimony based on scientific knowledge but also to testimony based on technical and other specialized knowledge