{"id":"W2522022217","doi":"","title":"Issues in Reasoning about Iffy Propositions: Reasoning Times in the Syntactic-Semantic Counter-Example Prompted Probabilistic Thinking and Reasoning Engine","year":2006,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Vlaamse regering","keywords":"Semantic reasoner; Psychology of reasoning; Context (archaeology); Antecedent (behavioral psychology); Probabilistic logic; Relation (database); Computer science; Notation; Inference; Epistemology; Cognitive science; Artificial intelligence; Natural language processing; Psychology; Linguistics; Philosophy; Knowledge representation and reasoning; Model-based reasoning; Social psychology","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003306405,0.0004709236,0.0006621646,0.000672207,0.0004293532,0.008076765,0.001246671,0.0002221218,0.0001872176],"category_scores_gemma":[0.004537558,0.0003362347,0.0001481298,0.001561054,0.0002129438,0.005682078,0.0004048599,0.0008800924,0.0003678461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001141946,"about_ca_system_score_gemma":0.0001446362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002799403,"about_ca_topic_score_gemma":0.0001369946,"domain_scores_codex":[0.995078,0.000436089,0.001486782,0.001119269,0.001141948,0.0007379101],"domain_scores_gemma":[0.9963312,0.002224501,0.0003648972,0.0008420291,0.0001083281,0.0001290267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002187338,0.000742158,0.9425754,0.00006006908,0.00002019859,0.000412443,0.0008821703,0.001027878,0.00007351008,0.01540256,0.001509935,0.03707497],"study_design_scores_gemma":[0.00206466,0.0003528945,0.3325881,0.004466543,0.00008026733,0.0004956621,0.002061805,0.02673511,0.0003250658,0.5752764,0.05372993,0.001823549],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891694,0.0008810313,0.0001876917,0.001003774,0.0001160357,0.0007922605,0.0002332157,0.0002016195,0.007414997],"genre_scores_gemma":[0.9938803,0.00002064015,0.005014096,0.000239805,0.0001347181,0.00007500446,0.0001820677,0.00007098717,0.0003823361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6099873,"threshold_uncertainty_score":0.999909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02854288489299894,"score_gpt":0.2890227040419,"score_spread":0.2604798191489011,"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."}}