{"id":"W113945247","doi":"","title":"People, Places and Things: Leveraging Insights from Distributed Cognition Theory to Enhance the User-Centered Design of Meteorological Information Systems","year":2005,"lang":"en","type":"article","venue":"UTAS Research Repository","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metis; Situated; Computer science; Cognition; Embodied cognition; Process (computing); Work (physics); Information system; Data science; Situated cognition; Human–computer interaction; Knowledge management; World Wide Web; Engineering; Artificial intelligence; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00665464,0.0009581736,0.0004262245,0.001798289,0.001986756,0.00655087,0.001950654,0.001522617,0.003090679],"category_scores_gemma":[0.01050172,0.0006894048,0.0009034747,0.0008657949,0.0096524,0.008727252,0.006061325,0.001969679,0.0003492612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002510521,"about_ca_system_score_gemma":0.002960541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00296168,"about_ca_topic_score_gemma":0.004095135,"domain_scores_codex":[0.9936802,0.004698047,0.0002131098,0.0004763196,0.0006906096,0.0002417113],"domain_scores_gemma":[0.9928809,0.005146939,0.0002616701,0.0009810275,0.0003694046,0.0003599539],"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":[0.0001336212,0.0004492272,0.005154499,0.0009495957,0.0001072048,0.00087569,0.1103003,0.02808512,0.005739122,0.6407386,0.003913918,0.2035532],"study_design_scores_gemma":[0.0001925815,0.0004496315,0.00232186,0.0005984148,0.0001450957,0.0006067632,0.03049512,0.07474096,0.004966934,0.7101997,0.1751926,0.00009029549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06673346,0.0009568305,0.8749148,0.005706512,0.0001570433,0.0003365223,0.00004384344,0.000581754,0.05056921],"genre_scores_gemma":[0.6294522,0.0007745117,0.3636463,0.0005130469,0.00003158486,0.000505366,0.00006590426,0.0001398858,0.004871347],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00665464,"threshold_uncertainty_score":0.0351935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04264303682438036,"score_gpt":0.3086642648766427,"score_spread":0.2660212280522623,"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."}}