{"id":"W3036232725","doi":"10.1101/2020.06.21.163352","title":"Lift observation conveys object weight distribution but partly enhances predictive lift planning","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Action Observation and Synchronization","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Fonds Wetenschappelijk Onderzoek","keywords":"Lift (data mining); Object (grammar); Weight distribution; Computer science; Artificial intelligence; Simulation; Computer vision; Mathematics; Engineering; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006280188,0.000723075,0.0007267327,0.0001880312,0.0003818118,0.0002831824,0.0005534115,0.001045146,0.0005223514],"category_scores_gemma":[0.0005725826,0.0008218816,0.0002070903,0.000854605,0.0001567623,0.0004637677,0.000263015,0.001196716,0.0004553222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005256947,"about_ca_system_score_gemma":0.0006116371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009059934,"about_ca_topic_score_gemma":0.000002137918,"domain_scores_codex":[0.995664,0.0005342106,0.00103441,0.001509229,0.0006268928,0.0006312433],"domain_scores_gemma":[0.9963235,0.00018311,0.001181192,0.001026693,0.0009278664,0.0003576927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001320497,0.001508398,0.4423743,0.00166144,0.003136977,0.0003369951,0.001604125,0.00275677,0.4455068,0.02357294,0.07616403,0.00005674776],"study_design_scores_gemma":[0.001674062,0.0002879089,0.8112785,0.0006611455,0.0003768747,5.701239e-8,0.0001426608,0.009700452,0.118292,0.00002587653,0.05581921,0.001741307],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7559904,0.0009536758,0.2294784,0.001525675,0.007435776,0.001506436,0.001490152,0.001479937,0.000139514],"genre_scores_gemma":[0.99515,0.0001385683,0.0008238759,0.0008336343,0.002216292,0.0005520103,0.00007281872,0.0001447429,0.00006800695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3689042,"threshold_uncertainty_score":0.9994232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03592605173267426,"score_gpt":0.270015225096103,"score_spread":0.2340891733634287,"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."}}