{"id":"W2400722199","doi":"10.31234/osf.io/qs735_v1","title":"The fan effect in overlapping data sets and logical inference","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Inference; Computer science; Scope (computer science); Cognition; Cognitive architecture; Logical conjunction; Logical data model; Artificial intelligence; Psychology; Programming language; Data modeling; Database","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":[0.01735463,0.001245911,0.001382382,0.001210784,0.001178414,0.002096378,0.001641014,0.001592559,0.01453968],"category_scores_gemma":[0.1826597,0.0008597854,0.00119214,0.000780466,0.004882361,0.01234511,0.003867596,0.003102243,0.0003893887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009980031,"about_ca_system_score_gemma":0.0009021427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001726715,"about_ca_topic_score_gemma":0.001803471,"domain_scores_codex":[0.9911718,0.004672138,0.0005508717,0.001092916,0.002110533,0.0004018056],"domain_scores_gemma":[0.5639542,0.4128475,0.008153828,0.01149414,0.002092659,0.001457614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.01798042,0.004644929,0.07033328,0.00557266,0.001041861,0.002815683,0.00964584,0.0479464,0.1305189,0.1635789,0.00427541,0.5416456],"study_design_scores_gemma":[0.001936605,0.01010526,0.06000086,0.0007413459,0.001216844,0.004004017,0.002791953,0.3241455,0.1440646,0.4425226,0.007925398,0.0005449787],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8777495,0.001393896,0.1047628,0.00088703,0.0001325533,0.0003714281,0.0002293532,0.000565659,0.01390781],"genre_scores_gemma":[0.9686517,0.000300865,0.02910705,0.0002178101,0.00005115341,0.0001683583,0.000140538,0.00009255674,0.001269944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01735463,"threshold_uncertainty_score":0.0917812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02625042527919315,"score_gpt":0.3625813832382834,"score_spread":0.3363309579590902,"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."}}