{"id":"W6929260710","doi":"10.48660/09110032","title":"Data analysis overview for IMR waveforms","year":2009,"lang":"en","type":"other","venue":"PIRSA","topic":"Heat shock proteins research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Perimeter Institute","funders":"","keywords":"Waveform; Data analysis; Noise (video); Signal processing; Data processing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002912156,0.0002141491,0.0003448773,0.0001874298,0.0000344996,0.00002896835,0.0008952694,0.0004090059,0.0007682232],"category_scores_gemma":[0.0001238937,0.0001773953,0.0001943021,0.0002279206,0.00003441995,0.000001495915,0.0003852713,0.0001068688,0.00007582728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001739478,"about_ca_system_score_gemma":0.0001303471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001690179,"about_ca_topic_score_gemma":0.001306521,"domain_scores_codex":[0.9985558,0.00003594796,0.0001765329,0.0006803101,0.0002054395,0.0003460173],"domain_scores_gemma":[0.9977312,0.000006519134,0.00007524949,0.002041105,0.00004090081,0.0001050291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002474558,0.00005708303,0.00004633847,0.0001673792,0.001229704,0.000003446639,0.00000281927,0.000002547191,0.00401394,0.00004007561,0.9575424,0.0368695],"study_design_scores_gemma":[0.0002228248,0.0001094643,0.00004337515,0.00002642293,0.0002289841,0.000001245027,0.000001764295,0.0001434104,0.001907385,0.00003100355,0.9970587,0.0002254201],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0002131756,0.05788145,0.04062456,0.0009842317,0.0002998128,0.003742907,0.01159673,0.0001585942,0.8844985],"genre_scores_gemma":[0.000914105,0.005466832,0.006164533,0.0004104819,0.001473294,0.0001133267,0.02123201,0.0003770455,0.9638484],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.07934983,"threshold_uncertainty_score":0.8411505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05908335803609956,"score_gpt":0.3657589016566359,"score_spread":0.3066755436205363,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}