{"id":"W3146474852","doi":"","title":"A HYBRID FEATURE SELECTION STRATEGY FOR IMAGE DEFINING FEATURES: TOWARDS INTERPRETATION OF OPTIC NERVE IMAGES","year":2005,"lang":"en","type":"article","venue":"Research Explorer (The University of Manchester)","topic":"Glaucoma and retinal disorders","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Artificial intelligence; Computer science; Optic nerve; Computer vision; Feature (linguistics); Feature selection; Feature extraction; Pattern recognition (psychology); Glaucoma; Image processing; Optic cup (embryology); Tomography; Image (mathematics); Radiology; Medicine; Anatomy","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":[0.0005355207,0.0001132071,0.0002384361,0.0002301293,0.0001713352,0.00001647983,0.0002132587,0.00007391648,0.00004418297],"category_scores_gemma":[0.000154693,0.00009163505,0.0001769119,0.000224007,0.0003450061,0.0002302785,0.00007894767,0.0003779753,0.0000152446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001085146,"about_ca_system_score_gemma":0.000154928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002623407,"about_ca_topic_score_gemma":0.000005296944,"domain_scores_codex":[0.998814,0.0001389621,0.0001265758,0.0002222358,0.0004083873,0.0002898384],"domain_scores_gemma":[0.998904,0.0001857847,0.00008869591,0.0002029103,0.0005367211,0.00008181301],"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":[0.0291391,0.002672076,0.01329849,0.009144147,0.00200991,0.0004510761,0.2102138,0.001561704,0.2563657,0.004569713,0.1504881,0.3200862],"study_design_scores_gemma":[0.01437446,0.009962011,0.4418578,0.003208114,0.001145987,0.0007990741,0.2795723,0.04049558,0.1916201,0.002629865,0.01333523,0.0009995003],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908201,0.000476173,0.003230923,0.003999736,0.00002199197,0.0006238833,0.00001361109,0.00002629484,0.0007873316],"genre_scores_gemma":[0.9936821,0.0001192529,0.005723165,0.00001702485,0.00004303579,0.000003078271,0.00003807965,0.00001543262,0.0003588113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4285592,"threshold_uncertainty_score":0.3736769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03438277563883097,"score_gpt":0.3113444651356508,"score_spread":0.2769616894968198,"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."}}