{"id":"W4229448254","doi":"10.18280/ts.390228","title":"Expression Identification and Emotional Classification of Students in Job Interviews Based on Image Processing","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Educational Technology and Pedagogy","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Expression (computer science); Set (abstract data type); Job interview; Computer science; Emotional expression; Histogram; Artificial intelligence; Representation (politics); Image processing; Graph; Artificial neural network; Psychology; Image (mathematics); Pattern recognition (psychology); Social psychology; Theoretical computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006982264,0.00006952338,0.00008304983,0.0002450457,0.0001326082,0.00003624249,0.0004543449,0.0000257053,0.0001275878],"category_scores_gemma":[0.00001291903,0.00007107497,0.00002039753,0.0002473897,0.00004150077,0.0002121132,0.0001136516,0.0001294989,0.000002688502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006350885,"about_ca_system_score_gemma":0.00006465538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003717061,"about_ca_topic_score_gemma":0.000002397154,"domain_scores_codex":[0.9988316,0.0001470218,0.0002985122,0.0002560056,0.0003776255,0.00008927246],"domain_scores_gemma":[0.999557,0.00005410329,0.000165722,0.0001575705,0.00004471998,0.00002092082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000175866,0.004033813,0.2410343,0.000272206,0.00001804417,0.000007363409,0.007054554,0.002539841,0.5694508,0.05041835,0.00153633,0.1234586],"study_design_scores_gemma":[0.0005828965,0.0001514127,0.9119447,0.00007645407,0.000003101922,0.000003074248,0.0003578778,0.07788198,0.006484746,0.002114135,0.0002986339,0.0001009798],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8616963,0.00005710856,0.1356347,0.002111239,0.00009245502,0.0002198137,0.000006163103,0.00003374598,0.0001484578],"genre_scores_gemma":[0.9963951,0.000002637648,0.003237267,0.0001624281,0.00001409145,0.0001234157,0.0000226794,0.000003517405,0.00003885646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6709105,"threshold_uncertainty_score":0.2898353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04899376216477591,"score_gpt":0.3387056407283213,"score_spread":0.2897118785635454,"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."}}