{"id":"W4405755248","doi":"10.1109/tmm.2024.3521796","title":"Context-Enriched Contrastive Loss: Enhancing Presentation of Inherent Sample Connections in Contrastive Learning Framework","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Context (archaeology); Presentation (obstetrics); Sample (material); Natural language processing; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00122933,0.0002776011,0.0004426361,0.0006419215,0.0001887816,0.00004209055,0.0001205742,0.000288336,0.001504282],"category_scores_gemma":[0.0007091672,0.0002787923,0.0001574211,0.000919158,0.0002301934,0.0001479327,0.000001564984,0.002195439,0.0001348221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001884531,"about_ca_system_score_gemma":0.00009409049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001743019,"about_ca_topic_score_gemma":0.0003382929,"domain_scores_codex":[0.9966627,0.001508164,0.0006430512,0.0005321637,0.0002497717,0.0004041646],"domain_scores_gemma":[0.9894403,0.009948961,0.0001615995,0.0001919277,0.0001746321,0.0000825695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001690355,0.0017902,0.005642125,0.000208521,0.001526157,0.0001031573,0.2083027,0.03734802,0.04684902,0.009732257,0.00020358,0.6866039],"study_design_scores_gemma":[0.02394581,0.007484136,0.2774397,0.008241894,0.001623611,0.0001774671,0.1961501,0.2639181,0.1907758,0.01213377,0.01299135,0.005118197],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.179212,0.000236284,0.8165002,0.0001672857,0.002588621,0.0004636991,0.00006225697,0.0002269543,0.0005426229],"genre_scores_gemma":[0.9901434,0.00001647048,0.008694054,0.0000672999,0.0001420213,0.0002373533,0.00001826463,0.00005445403,0.000626724],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8109313,"threshold_uncertainty_score":0.9999664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0375956953616343,"score_gpt":0.3789825457620014,"score_spread":0.341386850400367,"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."}}