{"id":"W1990011345","doi":"10.1002/cem.924","title":"Mathematical improvements to maximum likelihood parallel factor analysis: experimental studies","year":2005,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Covariance; Algorithm; Computer science; Set (abstract data type); Covariance matrix; Experimental data; Data set; Variety (cybernetics); Statistics; Data mining; Mathematics; Artificial intelligence","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":[0.01544725,0.001477271,0.0007993878,0.0008346699,0.000526891,0.001190276,0.001626119,0.0008789154,0.004824407],"category_scores_gemma":[0.08592981,0.0005087924,0.0006829486,0.002136976,0.001697022,0.002986091,0.001281847,0.002096353,0.001125173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000707224,"about_ca_system_score_gemma":0.0009660168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00196325,"about_ca_topic_score_gemma":0.001304007,"domain_scores_codex":[0.9905716,0.006385384,0.0002707024,0.0007155503,0.001936739,0.0001200709],"domain_scores_gemma":[0.9658322,0.02446466,0.00113856,0.004145211,0.00424111,0.0001781442],"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":[0.0008471388,0.000799383,0.002019426,0.001431766,0.0002267283,0.0002375367,0.0003125319,0.3507276,0.03521372,0.09521019,0.003739609,0.5092344],"study_design_scores_gemma":[0.0001409483,0.0005145537,0.001187606,0.00007109245,0.00003380505,0.0001916089,0.00004995968,0.9137416,0.03318662,0.0423434,0.008454863,0.00008377729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00872521,0.000471215,0.9889477,0.0002607233,0.00004382468,0.0000935269,0.00005706345,0.0003050158,0.001095674],"genre_scores_gemma":[0.1187731,0.001029176,0.8782953,0.0001292389,0.0001166583,0.0003417082,0.0002293333,0.0001419105,0.0009437084],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01544725,"threshold_uncertainty_score":0.08169383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03897354445166925,"score_gpt":0.3532158939684374,"score_spread":0.3142423495167682,"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."}}