{"id":"W6976496237","doi":"10.60692/qdyvc-wk536","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005258051,0.00009688555,0.0001031871,0.0002033643,0.0004510566,0.0003017644,0.0002202738,0.00002938845,0.000006917094],"category_scores_gemma":[0.000009091328,0.00009629464,0.00003588015,0.0002172799,0.00001131197,0.00157517,0.00009149884,0.00006504088,0.00001841063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001204606,"about_ca_system_score_gemma":0.00003491909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001917738,"about_ca_topic_score_gemma":3.677528e-8,"domain_scores_codex":[0.9989424,0.00008177253,0.0003744327,0.0001617428,0.0002888069,0.0001507884],"domain_scores_gemma":[0.9993266,0.00001503627,0.0002789288,0.0002238484,0.00009802346,0.000057536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001859401,0.000009993501,0.007781845,0.0005252571,0.00006898508,0.000001330865,0.6553741,0.08762415,0.00009193615,0.2069121,0.0004979004,0.04092646],"study_design_scores_gemma":[0.0008975905,0.00006097711,0.008578521,0.000008658287,0.000005925266,0.00002071183,0.01699443,0.9724239,0.00004195639,0.00006400566,0.0007782326,0.0001251317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04408694,0.000002758188,0.9530418,0.0001583347,0.0002623668,0.0005834919,0.00003016961,0.0002568414,0.001577274],"genre_scores_gemma":[0.9832704,7.815293e-8,0.01574731,0.0002549025,0.00002009002,0.00045544,0.00004316584,0.00000536696,0.0002032561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9391835,"threshold_uncertainty_score":0.3926781,"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."}}