{"id":"W4400906093","doi":"10.1016/j.cognition.2024.105845","title":"Using network science to provide insights into the structure of event knowledge","year":2024,"lang":"en","type":"article","venue":"Cognition","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; Population Health Research Institute; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Event (particle physics); Centrality; Psychology; Event structure; Experiential learning; Action (physics); Data science; Cognitive psychology; Computer science; Linguistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002616851,0.0009364143,0.0006565912,0.006019894,0.000836541,0.004000791,0.001048908,0.001000942,0.004202246],"category_scores_gemma":[0.02268151,0.0005450706,0.0009172007,0.005209272,0.00204341,0.009317734,0.002067912,0.002018627,0.0004701517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288878,"about_ca_system_score_gemma":0.001117591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004730167,"about_ca_topic_score_gemma":0.005696692,"domain_scores_codex":[0.9987829,0.0005811041,0.00006666932,0.0003358985,0.0001879841,0.00004538205],"domain_scores_gemma":[0.9795434,0.01651514,0.001551997,0.001437033,0.0006050598,0.00034726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00015198,0.0001489703,0.01853038,0.0006496653,0.0005982121,0.0002140864,0.00172019,0.1198738,0.004327306,0.7063807,0.003113964,0.1442908],"study_design_scores_gemma":[0.000007444292,0.00001426457,0.00245417,0.00006025592,0.00006739228,0.00006713442,0.0001935347,0.2110583,0.0006041637,0.7819971,0.003456494,0.00001980497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02812473,0.001170084,0.9642753,0.001283184,0.00004265089,0.00003940851,0.0008809158,0.0002738328,0.003909897],"genre_scores_gemma":[0.6028194,0.003387597,0.3887036,0.0002419494,0.000211781,0.0001948312,0.001655634,0.0001217352,0.002663408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006019894,"threshold_uncertainty_score":0.01405793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02068374872627941,"score_gpt":0.3215020364250331,"score_spread":0.3008182876987536,"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."}}