{"id":"W4399511717","doi":"10.48550/arxiv.2406.04508","title":"OCCAM: Towards Cost-Efficient and Accuracy-Aware Classification Inference","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Institute for Computing, Information and Cognitive Systems","keywords":"occam; Occam's razor; Computer science; Inference; Artificial intelligence; Image (mathematics); Contextual image classification; Machine learning; Pattern recognition (psychology); Data mining; Mathematics; Statistics; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006085537,0.002258634,0.00232386,0.002584457,0.001184043,0.003770879,0.00681611,0.003280826,0.004551555],"category_scores_gemma":[0.0308856,0.001312094,0.001387958,0.003403368,0.001624543,0.005466746,0.005786799,0.004459389,0.002795638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002685491,"about_ca_system_score_gemma":0.004197424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01037182,"about_ca_topic_score_gemma":0.01842271,"domain_scores_codex":[0.9952902,0.001425805,0.0001875514,0.0007454164,0.001976819,0.0003742544],"domain_scores_gemma":[0.9892399,0.005564744,0.0006397378,0.00262239,0.001591513,0.00034172],"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":[0.0007835757,0.0005098975,0.004762416,0.0003079624,0.0002394676,0.0001730746,0.0001920162,0.3262978,0.004934086,0.03110287,0.04286408,0.5878327],"study_design_scores_gemma":[0.00003599311,0.00002893044,0.000197791,0.00001427953,0.00001896638,0.00003974094,0.00001853482,0.9753565,0.001271155,0.02131957,0.001686955,0.0000115086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01681979,0.001184177,0.9679649,0.001254357,0.0001647745,0.000183875,0.0005041282,0.008720087,0.003203822],"genre_scores_gemma":[0.267805,0.0005069417,0.7221671,0.001198653,0.0004079705,0.0003679493,0.001742154,0.001318251,0.004486023],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01037182,"threshold_uncertainty_score":0.03218377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1992503980946852,"score_gpt":0.2612327154317885,"score_spread":0.0619823173371033,"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."}}