{"id":"W1524530684","doi":"10.1109/crv.2015.41","title":"Preprocessing Realistic Video for Contactless Heart Rate Monitoring Using Video Magnification","year":2015,"lang":"en","type":"article","venue":"","topic":"Image and Video Stabilization","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Magnification; Computer science; Computer vision; Artificial intelligence; Eulerian path; Preprocessor; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007864384,0.0001142091,0.0001359994,0.00007891216,0.0001621587,0.000404003,0.0002837416,0.0000486489,0.000002760885],"category_scores_gemma":[0.0004193847,0.0001086622,0.00003614008,0.0002794871,0.0000193852,0.00139302,0.00007671325,0.00004789115,0.00001150883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001134037,"about_ca_system_score_gemma":0.0001686915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001415727,"about_ca_topic_score_gemma":0.000006144149,"domain_scores_codex":[0.9988657,0.00006579922,0.000261664,0.0003944069,0.0001757926,0.0002366962],"domain_scores_gemma":[0.9987414,0.0001360117,0.0001058867,0.0004149372,0.000495752,0.0001060201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003878738,0.0006400109,0.0651957,0.001559327,0.00008831747,0.00002182597,0.01513727,0.03320228,0.6739339,0.09528498,0.01080157,0.1037469],"study_design_scores_gemma":[0.0008957571,0.00009259013,0.004949233,0.0001118199,0.00002570959,0.00001702335,0.0003004957,0.8319163,0.1515687,0.007630688,0.002124816,0.0003668602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02805952,0.00007453164,0.9698578,0.0003917015,0.000464159,0.0002984777,0.000001194139,0.0001931193,0.0006594695],"genre_scores_gemma":[0.866887,0.000002804456,0.1324,0.0001422378,0.000199904,0.00003354674,0.000005257249,0.00001352438,0.0003156468],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8388276,"threshold_uncertainty_score":0.4431116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1230170583174801,"score_gpt":0.3509241706939416,"score_spread":0.2279071123764615,"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."}}