{"id":"W4360598407","doi":"10.15659/uzalcbs2022.12798","title":"FARKLI MLS NOKTA BULUTU YOĞUNLUKLARININ VE KOMŞULUK YÖNTEMLERİNİN KONTROLLÜ SINIFLANDIRMAYA ETKİSİ","year":2022,"lang":"tr","type":"article","venue":"","topic":"Textile materials and evaluations","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008342516,0.000749329,0.0007580944,0.0008396735,0.001150554,0.003095701,0.0006210071,0.001152963,0.01620182],"category_scores_gemma":[0.0008787605,0.0004252003,0.0008226474,0.0007134124,0.001044444,0.001577722,0.001331839,0.001085236,0.005325457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001333868,"about_ca_system_score_gemma":0.00127823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005683261,"about_ca_topic_score_gemma":0.008279166,"domain_scores_codex":[0.9992946,0.00008593319,0.000057519,0.0001810094,0.0002442624,0.0001366627],"domain_scores_gemma":[0.9992886,0.0001129732,0.0001667516,0.00007197233,0.0002841866,0.00007550394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0020621,0.0003681153,0.03035839,0.001779145,0.0002163349,0.001591781,0.002129783,0.002168735,0.7067505,0.00652294,0.00523514,0.240817],"study_design_scores_gemma":[0.0001101367,0.002212589,0.09745399,0.0004601529,0.0003875305,0.003261606,0.006375171,0.005947576,0.6030917,0.005324113,0.2751265,0.0002488552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8924253,0.0179334,0.03813035,0.001975619,0.0006317942,0.0002218931,0.001550107,0.001398162,0.04573327],"genre_scores_gemma":[0.9031394,0.00866909,0.01928859,0.0006464379,0.0001140553,0.0001635438,0.002175338,0.0002711758,0.06553242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01620182,"threshold_uncertainty_score":0.05420053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03287278533726372,"score_gpt":0.2763110909445754,"score_spread":0.2434383056073117,"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."}}