{"id":"W4214937350","doi":"10.2139/ssrn.4047890","title":"Materials Informatics of Woven Fabric Composites: Effect of Different Dimensionality Reduction and Learning Methods","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Composite material; Woven fabric; Materials science; Dimensionality reduction; Reduction (mathematics); Curse of dimensionality; Artificial intelligence; Computer science; Mathematics; Geometry","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":[0.000720021,0.0005602345,0.0005021871,0.00113717,0.0003191808,0.0009882499,0.0003289453,0.0004063399,0.001634743],"category_scores_gemma":[0.002118147,0.0001601892,0.0007351773,0.0008105518,0.0003459656,0.001252021,0.0004286718,0.0005062548,0.0002638325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000223763,"about_ca_system_score_gemma":0.0004908899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001007014,"about_ca_topic_score_gemma":0.00101713,"domain_scores_codex":[0.9997223,0.000080511,0.00002439405,0.00006713974,0.0000700636,0.00003555411],"domain_scores_gemma":[0.998709,0.0006264909,0.0001240469,0.0002371595,0.0002431286,0.00006017206],"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":[0.002529815,0.001119916,0.01010992,0.0009207222,0.0002097163,0.0002199234,0.0004065746,0.348893,0.1396986,0.008459531,0.0024282,0.4850041],"study_design_scores_gemma":[0.00002237244,0.0002097281,0.006464111,0.00002817675,0.00008204858,0.00008046813,0.0001519901,0.9163375,0.07313289,0.002384319,0.001078973,0.0000273965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8173777,0.001193638,0.1743449,0.000287272,0.000116559,0.00005431631,0.0003751877,0.001112566,0.005137903],"genre_scores_gemma":[0.9190321,0.0006266743,0.07800869,0.00003690176,0.00003415688,0.00003265198,0.0006144041,0.0001081623,0.001506248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001634743,"threshold_uncertainty_score":0.005468786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007341413685022096,"score_gpt":0.2550690205372272,"score_spread":0.2477276068522051,"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."}}