{"id":"W1491540050","doi":"","title":"Visualizing causality in context using animation","year":2007,"lang":"en","type":"dissertation","venue":"Summit (Simon Fraser University)","topic":"Educational Tools and Methods","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Causality (physics); Context (archaeology); Animation; Computer science; Data science; Computer graphics (images); Human–computer interaction; Geography; Physics; Archaeology","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":[0.0009050206,0.0001741121,0.0002558051,0.0005658778,0.0004245103,0.00006454036,0.000272765,0.0004335227,0.0002614024],"category_scores_gemma":[0.0002681902,0.0002326564,0.0001018123,0.00111367,0.00008308984,0.0004559802,0.0000177756,0.0003248473,0.00001732869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008898063,"about_ca_system_score_gemma":0.0008481211,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01937316,"about_ca_topic_score_gemma":0.8195038,"domain_scores_codex":[0.9982627,0.0004334762,0.0002324056,0.0003399677,0.0003931402,0.0003382877],"domain_scores_gemma":[0.998962,0.0002416286,0.0002422433,0.0001411124,0.0002769694,0.0001360875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005516267,0.0006455266,0.6351922,0.0004353847,0.0001611055,0.0002384665,0.01424449,0.0001201156,0.0001999948,0.2749896,0.00600459,0.06721689],"study_design_scores_gemma":[0.0003379476,0.00001529638,0.01012483,0.0002133656,0.00006388062,9.705602e-10,0.4846034,0.00003723823,0.0004251239,0.000715947,0.5030941,0.0003688217],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8276919,0.0002025923,0.0002842961,0.00006835286,0.001064666,0.0003032194,0.00001427623,0.00005052097,0.1703203],"genre_scores_gemma":[0.9275958,0.0003245751,0.002120618,0.0001267369,0.0004370475,0.000001004452,0.0004861321,0.00003542477,0.06887269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8001306,"threshold_uncertainty_score":0.9871569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06988298008987238,"score_gpt":0.3968018580828597,"score_spread":0.3269188779929873,"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."}}