{"id":"W1588502470","doi":"10.20380/gi2001.13","title":"Interacting with Image Sequences: Detail-in-Context and Thumbnails","year":2001,"lang":"en","type":"article","venue":"Canada Human-Computer Communications Society","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Simon Fraser University","funders":"","keywords":"Thumbnail; Computer science; Context (archaeology); Image (mathematics); Presentation (obstetrics); Software; Artificial intelligence; Computer vision; Sequence (biology); Computer graphics (images); Information retrieval; Programming language","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":[0.0002462667,0.0001617331,0.0001872954,0.0000499287,0.0004931128,0.0003904193,0.001857495,0.00003868349,0.00001342991],"category_scores_gemma":[0.00001249206,0.0001512256,0.00004410258,0.00048048,0.0001834118,0.0007681008,0.0007467673,0.0002657552,0.000002683816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002082486,"about_ca_system_score_gemma":0.0003551949,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05282223,"about_ca_topic_score_gemma":0.4793045,"domain_scores_codex":[0.9987906,0.00010433,0.0003113754,0.0003238414,0.0002079289,0.0002619239],"domain_scores_gemma":[0.997844,0.0002339321,0.0001443095,0.001529884,0.0001399337,0.0001079572],"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":[0.00001244798,0.0008941168,0.06979399,0.0002565649,0.0006393549,0.0002323458,0.05623868,0.0008907731,0.001045126,0.5660109,0.1265072,0.1774785],"study_design_scores_gemma":[0.001139329,0.00006872547,0.005894239,0.0002979827,0.00002547009,0.0001596896,0.003720115,0.84877,0.00009168495,0.001023298,0.1380166,0.0007928674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04706774,0.0006387496,0.9375284,0.009863541,0.0001868711,0.0003694595,0.00001333917,0.000218064,0.004113855],"genre_scores_gemma":[0.8557383,0.0002140226,0.1404768,0.003228663,0.0000324683,0.00001459316,0.00004107012,0.0000135796,0.0002405049],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8478792,"threshold_uncertainty_score":0.9534851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0316161060161684,"score_gpt":0.2954380842079947,"score_spread":0.2638219781918263,"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."}}