{"id":"W4242731957","doi":"10.22215/etd/2009-09217","title":"Learning a complex category structure by classification and feature inference","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Library and Archives Canada","funders":"","keywords":"Inference; Feature (linguistics); Artificial intelligence; Computer science; Mathematics; Linguistics; Philosophy","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":[0.0001015923,0.0003016998,0.0002681455,0.0001490601,0.0002253402,0.0004277545,0.0006339577,0.0004564327,0.00004224196],"category_scores_gemma":[0.00006412521,0.0002624661,0.00004981826,0.0003622348,0.0000348804,0.0003818099,0.00003889057,0.0006916458,0.00001057411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005142171,"about_ca_system_score_gemma":0.000107931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002936988,"about_ca_topic_score_gemma":0.00003127095,"domain_scores_codex":[0.998506,0.00008076668,0.0002427916,0.00061822,0.0003323381,0.0002198475],"domain_scores_gemma":[0.9989119,0.00005392608,0.0003002305,0.0003961628,0.000244636,0.00009310689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001933009,0.00004813473,0.0001692716,0.0001463238,0.0000250462,0.000002619525,0.0009478882,4.288149e-7,0.2430387,0.05482863,0.03012871,0.6706449],"study_design_scores_gemma":[0.001101776,0.001115209,0.3970606,0.000393133,0.0001431288,0.00006545109,0.002583039,0.07499289,0.1762818,0.0573584,0.2846746,0.004230009],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01760635,0.004224337,0.8670954,0.007040163,0.0007450685,0.002017998,0.00006237601,0.004755205,0.09645307],"genre_scores_gemma":[0.9312871,0.0006257314,0.01740526,0.0003671205,0.00005668647,0.00002247719,0.002474503,0.00002542783,0.04773567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9136808,"threshold_uncertainty_score":0.9999828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01786393740647781,"score_gpt":0.2871712728053606,"score_spread":0.2693073353988828,"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."}}