{"id":"W1975056359","doi":"10.1145/2702123.2702510","title":"EnviroPulse","year":2015,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Valence (chemistry); Affect (linguistics); Global Positioning System; Human–computer interaction; Visualization; Augmented reality; Visual feedback; Real-time computing; Computer vision; Artificial intelligence; Psychology; Communication; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004376336,0.0006703078,0.0004817381,0.0004075609,0.0003737637,0.001091273,0.001135106,0.0006418885,0.06520498],"category_scores_gemma":[0.00177102,0.0004067293,0.0003582472,0.0002510494,0.0002936423,0.001746424,0.001845027,0.0006913778,0.01834218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002238104,"about_ca_system_score_gemma":0.0003649255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001071535,"about_ca_topic_score_gemma":0.001862526,"domain_scores_codex":[0.9997339,0.00002738638,0.0000115838,0.00009897104,0.00009646306,0.00003174689],"domain_scores_gemma":[0.9994536,0.0001516856,0.00003076709,0.000137639,0.0001290304,0.00009729512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003878054,0.000522434,0.00965135,0.001241676,0.0001483435,0.001006984,0.002109956,0.001177842,0.1037957,0.01030664,0.3184733,0.5476878],"study_design_scores_gemma":[0.0006501715,0.002373509,0.05768708,0.0002892163,0.0002597403,0.003206563,0.0007177838,0.05730861,0.07696722,0.01703588,0.7831397,0.0003644503],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"software","genre_gemma":"other","genre_scores_codex":[0.1115623,0.001735108,0.2447502,0.001171134,0.0006775629,0.001717007,0.01909666,0.3197561,0.299534],"genre_scores_gemma":[0.560709,0.001247748,0.1571757,0.00215208,0.0002920039,0.00174659,0.02572266,0.01627963,0.2346746],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.06520498,"threshold_uncertainty_score":0.2181324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05524807603811409,"score_gpt":0.2774483869715232,"score_spread":0.2222003109334091,"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."}}