{"id":"W2589084111","doi":"10.7287/peerj.preprints.2749v1","title":"Comprehensive model optimization in pulp quality prediction: a machine learning approach","year":2017,"lang":"en","type":"article","venue":"","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Kappa number; Artificial intelligence; Feature selection; Fuzzy logic; Pulp (tooth); Genetic algorithm; Machine learning; Data mining; Algorithm; Mathematics; Mathematical optimization; Engineering","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":[0.001860629,0.001013431,0.001916166,0.001246839,0.0004066919,0.001243749,0.000900965,0.001005738,0.0008532396],"category_scores_gemma":[0.003154317,0.0005141745,0.001135105,0.001180125,0.0004950051,0.0009712578,0.000731202,0.0008837091,0.0001524635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007518371,"about_ca_system_score_gemma":0.00115131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008746232,"about_ca_topic_score_gemma":0.005663054,"domain_scores_codex":[0.9993911,0.0002438798,0.00003872852,0.0001299029,0.0001387248,0.00005772469],"domain_scores_gemma":[0.9989542,0.0007412613,0.00007758952,0.00006754724,0.0001372821,0.00002200639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001609266,0.00001813447,0.0003953713,0.00002038474,0.00003777289,0.00002009747,0.00001221487,0.9855825,0.0003925742,0.0007199299,0.00008094756,0.01270397],"study_design_scores_gemma":[0.000001350033,0.00001147467,0.00008554598,0.000001901352,0.000006397106,0.000002749858,0.000002349908,0.9991207,0.0001248622,0.0005887634,0.00005168392,0.00000231326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04814353,0.0007683052,0.9492077,0.0002032091,0.00001476605,0.00004682224,0.00008647497,0.0005483382,0.0009808654],"genre_scores_gemma":[0.8732419,0.0005228738,0.124411,0.00008473642,0.00003453907,0.0001621636,0.0002796421,0.00006314745,0.001199942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008746232,"threshold_uncertainty_score":0.01739067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0822208288455922,"score_gpt":0.2981967506874901,"score_spread":0.2159759218418979,"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."}}