{"id":"W4386307227","doi":"10.18280/ts.400415","title":"The Influence of Visual Features in Product Images on Sales Volume: A Machine Learning Approach to Extract Color and Deep Learning Super Sampling Features","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Volume (thermodynamics); Artificial intelligence; Computer science; Product (mathematics); Deep learning; Sampling (signal processing); Computer vision; Pattern recognition (psychology); Machine learning; Mathematics","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":[0.002916448,0.0001716657,0.0002320401,0.0002976541,0.0004717831,0.0002598228,0.000444107,0.00004818829,0.00001442511],"category_scores_gemma":[0.001638009,0.0001111837,0.00005721186,0.001031552,0.0001508161,0.0001315578,0.0001741306,0.0004116245,0.0000173926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002522829,"about_ca_system_score_gemma":0.00002354592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008717195,"about_ca_topic_score_gemma":0.00005910745,"domain_scores_codex":[0.9977795,0.00019816,0.0004616533,0.0005120006,0.0007307012,0.0003179407],"domain_scores_gemma":[0.9982361,0.001192328,0.0001658597,0.0001967727,0.0001355657,0.00007335622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000442344,0.0003936509,0.09914026,0.00005400429,0.00004041369,0.000007211519,0.00570423,0.5278412,0.09415474,0.003305633,0.003645077,0.2652712],"study_design_scores_gemma":[0.0003294713,0.0005672683,0.9378327,0.0000879053,0.00001252309,0.00001551818,0.001412089,0.04413367,0.002695841,0.001099391,0.01153711,0.0002765362],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963327,0.000238032,0.001583512,0.0008807438,0.00001563755,0.0005538927,0.000009220001,0.0001238937,0.0002623386],"genre_scores_gemma":[0.9948054,0.0000512265,0.004161729,0.00005183,0.00004882867,0.0001446755,0.0000164852,0.00001715803,0.0007026946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8386924,"threshold_uncertainty_score":0.4533938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0542374206172087,"score_gpt":0.3579367686464595,"score_spread":0.3036993480292508,"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."}}