{"id":"W4417306435","doi":"10.48550/arxiv.2505.19328","title":"BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change","year":2025,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Modalities; Metadata; Personalization; Psychological intervention; Benchmarking; Baseline (sea); Affect (linguistics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007830425,0.002029796,0.001010675,0.002441703,0.0007750674,0.001172908,0.001630951,0.001908152,0.01127461],"category_scores_gemma":[0.003390228,0.0002930969,0.001277832,0.001467362,0.0004327907,0.0008385626,0.001731757,0.001404593,0.009276441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001543961,"about_ca_system_score_gemma":0.001085341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03767814,"about_ca_topic_score_gemma":0.1188594,"domain_scores_codex":[0.9991147,0.0001597671,0.00008882715,0.0002382074,0.0002474351,0.000151146],"domain_scores_gemma":[0.9988964,0.0003027229,0.000135473,0.0002117622,0.000330505,0.0001230561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009190345,0.0005821544,0.01355004,0.004361784,0.0002773954,0.000770496,0.0005565819,0.001522662,0.009860966,0.0010664,0.8350633,0.1314691],"study_design_scores_gemma":[0.0004246696,0.0005151294,0.113096,0.002290294,0.0003544638,0.002081818,0.002293923,0.01845607,0.01461648,0.002561046,0.8429821,0.000328065],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02582319,0.003525604,0.005189731,0.0006184179,0.0007616731,0.001425098,0.9482436,0.00628067,0.008131924],"genre_scores_gemma":[0.02586521,0.0008021149,0.01197545,0.0003172473,0.0001018884,0.001721189,0.9547593,0.0002063326,0.004251317],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03767814,"threshold_uncertainty_score":0.07491761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3196006877257187,"score_gpt":0.2833865046842162,"score_spread":0.03621418304150248,"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."}}