{"id":"W2156705594","doi":"10.1109/ast.2009.18","title":"Artificial K-lines","year":2009,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Artificial neural network; Artificial intelligence; Computer science; Causality (physics); Physics","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":[0.00007215649,0.0000453722,0.0000462362,0.00002533056,0.00004044176,0.00008852341,0.0003129305,0.00002244528,0.00001764267],"category_scores_gemma":[0.000008749292,0.00003624132,0.00002046734,0.0001257594,0.000006565937,0.0001639085,0.00002087495,0.00004162918,0.0001706966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003477654,"about_ca_system_score_gemma":0.0000180223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004127808,"about_ca_topic_score_gemma":0.000001456374,"domain_scores_codex":[0.9995658,0.000008205122,0.00008685444,0.0001391119,0.00008356471,0.0001164769],"domain_scores_gemma":[0.9997029,0.000009080236,0.00001161047,0.0002042245,0.00002915249,0.00004308702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[4.663847e-7,0.00001677934,0.000007258804,1.986388e-7,4.740323e-7,0.000001941165,0.00003364871,0.00004517387,0.001797985,0.6985037,0.001125998,0.2984664],"study_design_scores_gemma":[0.00003754332,0.0001043805,0.0008705258,0.000004965795,0.000001217977,0.000009734625,0.000004777028,0.3963295,0.01896797,0.5818217,0.001673494,0.0001742042],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005677763,0.00001811378,0.9694298,0.004576865,0.0001162978,0.00001651448,6.699572e-8,0.000240141,0.01992445],"genre_scores_gemma":[0.9263577,0.000001984384,0.07145474,0.001630681,0.00007145759,4.905494e-7,1.903012e-7,8.590415e-7,0.0004819217],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9206799,"threshold_uncertainty_score":0.2194017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03896102344094872,"score_gpt":0.2782948618796646,"score_spread":0.2393338384387159,"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."}}