{"id":"W4394814068","doi":"10.1007/s40747-024-01417-z","title":"Keyframe recommendation based on feature intercross and fusion","year":2024,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Program of Guizhou Province; Petroleum Technology Research Centre; National Natural Science Foundation of China","keywords":"Computer science; Feature extraction; Redundancy (engineering); Artificial intelligence; Frame (networking); Computational intelligence; Feature (linguistics); Pattern recognition (psychology); Computer vision; Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0008328406,0.001089932,0.001442275,0.002533376,0.0007251,0.0009256309,0.001241158,0.000982988,0.001922924],"category_scores_gemma":[0.002849697,0.0003586925,0.001312415,0.002446225,0.0002737901,0.001969557,0.0007151346,0.0007936561,0.001139635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006865695,"about_ca_system_score_gemma":0.0009788931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01338523,"about_ca_topic_score_gemma":0.01291673,"domain_scores_codex":[0.9985451,0.0001408103,0.0001076602,0.0004495923,0.0005961512,0.0001608421],"domain_scores_gemma":[0.9984788,0.0002612348,0.0001276802,0.0003343166,0.0007082323,0.00008964664],"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":[0.0005004609,0.0002901399,0.004827448,0.0002259149,0.0002000432,0.0001657615,0.0001714732,0.03349702,0.04234359,0.00263419,0.009445192,0.9056988],"study_design_scores_gemma":[0.00006783428,0.0003728861,0.005111269,0.00004074536,0.0002351682,0.0005065269,0.000198473,0.9330534,0.04798719,0.003765738,0.008559046,0.0001016976],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03934035,0.001003087,0.9550107,0.0001452464,0.0001210442,0.0002029954,0.0003794564,0.002004452,0.001792815],"genre_scores_gemma":[0.5256699,0.0008173934,0.4655659,0.0001545544,0.0001763386,0.0001908197,0.001845055,0.0001603519,0.005419719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01338523,"threshold_uncertainty_score":0.02661467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03847584680883407,"score_gpt":0.2889303341603381,"score_spread":0.2504544873515041,"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."}}