{"id":"W6901860161","doi":"10.60692/kervm-vp575","title":"Pre-trained Models for SMP Classification and Segmentation","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Random forest; Mixture model; Naive Bayes classifier; Python (programming language); Artificial neural network; Classifier (UML); Bayesian probability; Pattern recognition (psychology); Segmentation","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":[0.0009096651,0.00335018,0.001023465,0.001542787,0.0006912978,0.001523751,0.002516513,0.002186816,0.01740536],"category_scores_gemma":[0.003777313,0.0007376264,0.002718522,0.001578932,0.0005365959,0.001720814,0.001237822,0.00392328,0.0267576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001814113,"about_ca_system_score_gemma":0.002065474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0341584,"about_ca_topic_score_gemma":0.05000118,"domain_scores_codex":[0.9990804,0.0001299186,0.00004853329,0.0004010596,0.0001704092,0.0001696002],"domain_scores_gemma":[0.9991617,0.0002558612,0.00003728598,0.0002046168,0.0002933849,0.00004713228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000617359,0.0005019094,0.005970632,0.0008257293,0.0003339536,0.0004257546,0.0002083006,0.2055774,0.008856366,0.002746399,0.3870624,0.3868739],"study_design_scores_gemma":[0.00008765931,0.0001518954,0.005166893,0.0002906924,0.0001043049,0.0002136423,0.0002214542,0.8959565,0.01335196,0.005676197,0.07867861,0.0001003433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1314648,0.007094983,0.3445449,0.002536955,0.004062984,0.001314794,0.3415603,0.1263942,0.04102626],"genre_scores_gemma":[0.2340044,0.001483725,0.1861936,0.001029865,0.0003846259,0.001489743,0.5407163,0.004322994,0.03037476],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0341584,"threshold_uncertainty_score":0.06791919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06226005648060976,"score_gpt":0.2379921766388127,"score_spread":0.1757321201582029,"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."}}