{"id":"W4401927927","doi":"10.18280/jesa.570427","title":"Product Matching with Two-Branch Neural Network Embedding","year":2024,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Embedding; Artificial neural network; Product (mathematics); Matching (statistics); Computer science; Mathematics; Artificial intelligence; Statistics; Geometry","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001169718,0.0003039495,0.0003893696,0.0002245221,0.0006431963,0.00349623,0.001103321,0.0000323621,0.0000453008],"category_scores_gemma":[0.00005617911,0.0002128017,0.000171977,0.001410925,0.00007153524,0.002026468,0.000273621,0.0005938063,0.0001490075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001133457,"about_ca_system_score_gemma":0.0001688928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003295037,"about_ca_topic_score_gemma":0.00001295942,"domain_scores_codex":[0.9972686,0.0003215184,0.00055007,0.0005637823,0.0006413737,0.0006546712],"domain_scores_gemma":[0.9986741,0.0001705759,0.0002170696,0.000595975,0.0001174844,0.0002247975],"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":[0.000007847692,0.00003282327,0.001604975,0.000227183,0.0003836049,0.001742683,0.00217522,0.1052193,0.0006242694,0.006651354,0.009134562,0.8721962],"study_design_scores_gemma":[0.0002743292,0.0001517939,0.02132617,0.001659442,0.0001213245,0.009278432,0.00006704016,0.9562868,0.00007712804,0.007396637,0.002873106,0.0004877801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.296274,0.005819812,0.691499,0.001211579,0.001622661,0.0001366523,0.000005670487,0.001313833,0.002116774],"genre_scores_gemma":[0.9012648,0.00005489165,0.09630774,0.0001285556,0.001260423,0.000005075362,0.00000310246,0.00004822697,0.0009272151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8717085,"threshold_uncertainty_score":0.9975382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01659119362090604,"score_gpt":0.2683051606760398,"score_spread":0.2517139670551337,"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."}}