{"id":"W4393656946","doi":"10.5281/zenodo.10013329","title":"VISIONE Feature Repository for VBS: Multi-Modal Features and Detected Objects from VBSLHE Dataset","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Modal; Feature (linguistics); Computer science; Artificial intelligence; Pattern recognition (psychology); Philosophy; Materials science; Linguistics; Polymer chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004432256,0.0003130891,0.0002906807,0.0003890989,0.002926508,0.002917467,0.002050846,0.0003164601,0.00009312794],"category_scores_gemma":[0.0008635045,0.0003068334,0.00006397675,0.0005663484,0.0001883256,0.000526217,0.002258826,0.000699616,0.001390296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009420096,"about_ca_system_score_gemma":0.00002164871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001513327,"about_ca_topic_score_gemma":0.00000628618,"domain_scores_codex":[0.9974697,0.0003202625,0.0002879854,0.001066247,0.0004274708,0.0004282995],"domain_scores_gemma":[0.9978806,0.00009995823,0.0002726286,0.001051356,0.0004916397,0.0002037741],"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":[0.00003933279,0.00005265596,9.697356e-8,0.0001589504,0.00005626377,0.00003449463,0.0001656007,0.000004343319,0.001025752,0.00000995594,0.9681513,0.03030126],"study_design_scores_gemma":[0.0007220426,0.0001646229,0.0001496229,0.0001530523,0.00004628149,0.0003931124,0.00006692273,0.001343539,0.0006314254,0.0001106801,0.995854,0.0003646661],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005350801,0.0004374347,0.01167904,0.0003067387,0.0007910806,0.000542592,0.9850829,0.001010493,0.00009620093],"genre_scores_gemma":[0.000130531,0.0001858662,0.009454885,0.00009461837,0.0003850128,2.829828e-7,0.9886104,0.0006057692,0.000532639],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02993659,"threshold_uncertainty_score":0.9999384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02258615830888217,"score_gpt":0.2543638961721714,"score_spread":0.2317777378632893,"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."}}