{"id":"W2166886337","doi":"10.48550/arxiv.1207.1393","title":"Learning about individuals from group statistics","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Binary classification; Artificial intelligence; Probabilistic logic; Binary number; Computer science; Classifier (UML); Group (periodic table); Fraction (chemistry); Machine learning; Object (grammar); Task (project management); Baseline (sea); Probabilistic classification; Bayesian probability; Feature (linguistics); Feature vector; Pattern recognition (psychology); Mathematics; Naive Bayes classifier; Support vector machine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003015248,0.0002763544,0.0002883771,0.0001704977,0.0001829966,0.0002195657,0.001730443,0.0003155084,0.0001033869],"category_scores_gemma":[0.0000589532,0.0003165242,0.0001167323,0.0003661302,0.0001047859,0.0004416328,0.001836942,0.0008427573,0.0002437676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001516507,"about_ca_system_score_gemma":0.0001015135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001874022,"about_ca_topic_score_gemma":0.000004113825,"domain_scores_codex":[0.9982746,0.0002194904,0.0002179947,0.0007958121,0.0001413665,0.0003506857],"domain_scores_gemma":[0.9982063,0.0001845384,0.0003755053,0.0009026765,0.0001524127,0.0001786134],"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":[0.00001666138,0.0002665435,0.03732116,0.0001084952,0.0002305482,0.000139372,0.001240897,0.001329531,0.0003575541,0.9251332,0.001489492,0.03236649],"study_design_scores_gemma":[0.001348827,0.0002008501,0.100545,0.000385319,0.000402231,0.00000663559,0.0002974906,0.3596619,0.004798619,0.4924198,0.03708965,0.002843711],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02017746,0.0001656778,0.9767651,0.00003911297,0.0003861744,0.0001829537,0.00009891366,0.000623394,0.001561215],"genre_scores_gemma":[0.9659745,0.0004356943,0.03151404,0.00007032719,0.0001153733,0.000001362008,0.0001518387,0.00001865354,0.001718285],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.945797,"threshold_uncertainty_score":0.9999287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06607524641510701,"score_gpt":0.2026243560489335,"score_spread":0.1365491096338265,"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."}}