{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002728855,0.00222481,0.001204691,0.003557458,0.000815023,0.002455845,0.002404812,0.0007385081,0.2000043],"category_scores_gemma":[0.009200899,0.0007564065,0.001241448,0.002809949,0.0003687478,0.001884635,0.001619805,0.001385333,0.09320276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007100608,"about_ca_system_score_gemma":0.001335584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0021972,"about_ca_topic_score_gemma":0.00252028,"domain_scores_codex":[0.9983418,0.0001629693,0.0002735317,0.0004210944,0.00062714,0.0001734231],"domain_scores_gemma":[0.9956013,0.001118213,0.0002118485,0.001287009,0.00158923,0.0001923902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001015718,0.0001525461,0.001833961,0.001146017,0.0001461429,0.0003219349,0.0002467773,0.002010082,0.0268465,0.006164545,0.488765,0.4713507],"study_design_scores_gemma":[0.0003571544,0.0001733556,0.005920253,0.0003665898,0.0001534042,0.0008951637,0.0001865092,0.04237847,0.1016416,0.00937676,0.838306,0.0002448047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"empirical","genre_scores_codex":[0.00387651,0.0002846186,0.3974817,0.0003830854,0.0003761182,0.001144955,0.1014573,0.4591651,0.0358306],"genre_scores_gemma":[0.03159545,0.0004202123,0.6419776,0.0006615199,0.0002342814,0.003606887,0.1633221,0.1051593,0.05302257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2000043,"threshold_uncertainty_score":0.6690811,"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."}}