{"id":"W1929604979","doi":"10.1007/978-3-540-75696-5_15","title":"Deployment Experience Toward Core Abstractions for Context Aware Applications","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Context (archaeology); Set (abstract data type); Software deployment; Core (optical fiber); Process (computing); Iterative and incremental development; Software engineering; Context model; Data science; Human–computer interaction; Programming language; Artificial intelligence","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":[0.002416654,0.0008589412,0.0004403978,0.0003143139,0.0005683123,0.00281596,0.001407633,0.0008468635,0.005528462],"category_scores_gemma":[0.006025078,0.0008211343,0.0003217435,0.0004476255,0.0004858032,0.004560788,0.002425807,0.003289821,0.002255985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005216679,"about_ca_system_score_gemma":0.0006859864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002084556,"about_ca_topic_score_gemma":0.00248948,"domain_scores_codex":[0.9983627,0.0005214504,0.000088924,0.000200001,0.0006043112,0.0002225687],"domain_scores_gemma":[0.9975915,0.0008121939,0.00008650791,0.0006073432,0.0006094592,0.0002930722],"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":[0.00201748,0.001147749,0.008601025,0.001066266,0.0001010055,0.001177051,0.01789214,0.02381832,0.1687018,0.09274594,0.04811037,0.634621],"study_design_scores_gemma":[0.0002685031,0.002012931,0.00832023,0.0005301901,0.000325468,0.002747985,0.007826908,0.368969,0.1788108,0.04794656,0.3820826,0.0001587818],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2954978,0.001462177,0.6397127,0.001870959,0.0003595195,0.0002469634,0.0002900746,0.0159148,0.04464509],"genre_scores_gemma":[0.6969359,0.001389584,0.2751562,0.0004249726,0.0000687762,0.0001336946,0.0009640511,0.003068532,0.02185836],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005528462,"threshold_uncertainty_score":0.01849449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09730368469260686,"score_gpt":0.3294852479283288,"score_spread":0.2321815632357219,"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."}}