{"id":"W4406657658","doi":"10.1007/978-3-031-77392-1_5","title":"Contrastive Loss Based on Contextual Similarity for Image Classification","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; Similarity (geometry); Artificial intelligence; Pattern recognition (psychology); Image (mathematics); Natural language processing; Information retrieval","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009992875,0.0006441919,0.001203072,0.000762783,0.0002721476,0.0008370402,0.001138329,0.0007971522,0.002747148],"category_scores_gemma":[0.001681384,0.0001773767,0.0005683188,0.001020241,0.0004617011,0.001312158,0.0009880465,0.001147655,0.0009638848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005330908,"about_ca_system_score_gemma":0.0005166824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0011651,"about_ca_topic_score_gemma":0.001669904,"domain_scores_codex":[0.9994872,0.0001038884,0.00002286094,0.00009567488,0.0002310794,0.00005932165],"domain_scores_gemma":[0.9994951,0.0001790138,0.00003894133,0.0001133349,0.0001456843,0.00002784347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007425225,0.0003653218,0.0007923826,0.0002396571,0.0001228898,0.0000993613,0.00004135258,0.06052341,0.09036939,0.02139384,0.008506462,0.8168035],"study_design_scores_gemma":[0.00002066643,0.0002312457,0.001278262,0.00001896024,0.0000691905,0.0002491843,0.00001533936,0.9640457,0.0233328,0.007144603,0.003577134,0.0000169199],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02612412,0.00180162,0.969092,0.0001576782,0.0001480391,0.00005703401,0.00009291444,0.0004788401,0.002047689],"genre_scores_gemma":[0.4942369,0.002190575,0.4903708,0.0003414126,0.0006029878,0.000171495,0.0008167014,0.0003077739,0.01096139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002747148,"threshold_uncertainty_score":0.009190142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02387673156161369,"score_gpt":0.2825821431947735,"score_spread":0.2587054116331598,"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."}}