{"id":"W2293264864","doi":"","title":"Good Moods: Outlook, Affect and Mood in Dynemotion and the Mind Module","year":2009,"lang":"en","type":"article","venue":"Loading...","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alchemy (Canada)","funders":"","keywords":"Affordance; Avatar; Mood; Character (mathematics); Human–computer interaction; Context (archaeology); Computer science; Focus (optics); Affect (linguistics); Feature (linguistics); Psychology; Cognitive science; Communication; Social psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002316409,0.00009052475,0.0001560477,0.00006554439,0.00007171703,0.00004223637,0.00005506765,0.00008706458,0.0002864613],"category_scores_gemma":[0.00005111,0.00006951785,0.00003406196,0.00009353185,0.00007125884,0.00006979051,0.00001308972,0.0001679462,0.00007657253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002008525,"about_ca_system_score_gemma":0.000004021337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001485249,"about_ca_topic_score_gemma":0.000136267,"domain_scores_codex":[0.9993314,0.0001184617,0.0001337587,0.0001918501,0.0000666506,0.0001579435],"domain_scores_gemma":[0.9996325,0.0001373092,0.00005134352,0.0001255082,0.00001189391,0.00004137998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002041611,0.001207636,0.09083755,0.0000394322,0.0002898203,0.00005540554,0.09179141,0.00004253757,0.004183452,0.279627,0.01290295,0.5169812],"study_design_scores_gemma":[0.009105506,0.0004183685,0.9666639,0.00007519405,0.00006222579,0.00009059903,0.004922099,0.001085429,0.0006339768,0.01131161,0.005286966,0.0003441558],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8938703,0.0004475353,0.0001147254,0.003140907,0.0004214851,0.0002357867,0.000001821771,0.00002324234,0.1017442],"genre_scores_gemma":[0.9967534,0.00002785914,0.00006007305,0.0005448645,0.000104186,0.000009923028,0.000001733904,0.000006748662,0.002491206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8758263,"threshold_uncertainty_score":0.313655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02143457046337428,"score_gpt":0.3305348164534177,"score_spread":0.3091002459900434,"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."}}