{"id":"W4391637182","doi":"10.32920/25190984.v1","title":"Classifying Negative Objects With Neural Networks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Lexicon; Object (grammar); Computer science; Point (geometry); Artificial intelligence; Artificial neural network; Image (mathematics); Space (punctuation); Term (time); Pattern recognition (psychology); Computer vision; Mathematics; Physics; Geometry","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":[0.001722954,0.001865596,0.0009374266,0.003202951,0.0007081925,0.002785749,0.001738794,0.002018771,0.002272536],"category_scores_gemma":[0.005967916,0.0004942224,0.0009695879,0.001588122,0.0009102693,0.002383694,0.001359697,0.001312285,0.001431257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001625821,"about_ca_system_score_gemma":0.0005773814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006919725,"about_ca_topic_score_gemma":0.008256899,"domain_scores_codex":[0.9986149,0.000281534,0.00009753033,0.0004248113,0.0003802967,0.000200885],"domain_scores_gemma":[0.9977298,0.001005121,0.0002685303,0.0002606868,0.0006568601,0.00007895326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009101029,0.0003944061,0.02401243,0.0004250243,0.0002512641,0.0007126906,0.0003589792,0.07374623,0.01101705,0.01015516,0.02866099,0.8493556],"study_design_scores_gemma":[0.00002573736,0.00009447018,0.005694541,0.0001175113,0.00008168122,0.0002371988,0.0002999812,0.9621832,0.007681921,0.01696958,0.006578411,0.00003569389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5261112,0.006667415,0.4181634,0.002869972,0.001321896,0.0005833572,0.003618514,0.005772247,0.03489184],"genre_scores_gemma":[0.8776768,0.0009583113,0.1053394,0.0006105108,0.000416727,0.000195385,0.005264885,0.0001563008,0.009381757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006919725,"threshold_uncertainty_score":0.0137589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0252899597102265,"score_gpt":0.2760285768144012,"score_spread":0.2507386171041747,"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."}}