{"id":"W4309598565","doi":"10.1145/3565516.3565523","title":"The Colour of Horror","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Carleton University","funders":"","keywords":"Cluster analysis; Weighting; Computer science; Palette (painting); Theme (computing); Artificial intelligence; Computer vision; Computer graphics (images); Art; Visual arts; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":{"n_in":0,"stratum":"aff_core","weight":5595.2375,"opus":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"high","reason":"Computational extraction of color palettes from film trailers; a media analysis question."},"gpt":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"high","reason":"The work uses computational methods to analyze color palettes in films."},"grok":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"high","reason":"Computational colour-palette analysis of film trailers is media/CS application, not metaresearch."}},"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005802949,0.0006949364,0.000369392,0.004363594,0.000888033,0.001844068,0.0004398803,0.0004316663,0.01316745],"category_scores_gemma":[0.003851807,0.0003738941,0.0007180527,0.002022052,0.0006921405,0.001387731,0.001235394,0.0008593819,0.00326031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004992763,"about_ca_system_score_gemma":0.00033954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002207093,"about_ca_topic_score_gemma":0.004356117,"domain_scores_codex":[0.999657,0.00006584745,0.00001955971,0.00009457902,0.000122553,0.00004048075],"domain_scores_gemma":[0.9988949,0.0003249507,0.00007300393,0.0001640747,0.0004598427,0.00008322344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001181436,0.00006312668,0.01430693,0.002052461,0.0001569989,0.0004470109,0.006335991,0.006618119,0.07136586,0.01790894,0.05747244,0.8220907],"study_design_scores_gemma":[0.0001774368,0.0004950894,0.1327658,0.001357789,0.0003733314,0.001885474,0.007612238,0.08777435,0.08264551,0.03486308,0.6495765,0.0004732983],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.2429316,0.00385669,0.5876092,0.001215943,0.002675205,0.001121093,0.01538533,0.01235863,0.1328464],"genre_scores_gemma":[0.4781208,0.00166575,0.4748455,0.000299868,0.0005331988,0.0006892312,0.008226804,0.003697154,0.03192168],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01316745,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005337281085710543,"score_gpt":0.1934269462387848,"score_spread":0.1880896651530743,"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."}}