{"id":"W2121436341","doi":"","title":"TIP spatial index: efficient access to digital libraries in a context-aware mobile system","year":2012,"lang":"en","type":"article","venue":"Research Commons (University of Waikato)","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science; Spatial contextual awareness; Index (typography); Context (archaeology); Spatial analysis; Spatial database; Mobile device; Mobile computing; Digital library; World Wide Web; Tourism; Multimedia; Information retrieval; Telecommunications; Geography; Artificial intelligence; Remote sensing","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":[0.0004189998,0.0003634045,0.000506188,0.00112534,0.0008958068,0.002141492,0.001192537,0.000708601,0.003104941],"category_scores_gemma":[0.001615719,0.000226309,0.000370236,0.001083044,0.0007317296,0.002959909,0.003087142,0.000516105,0.001040806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004776842,"about_ca_system_score_gemma":0.001110635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00540069,"about_ca_topic_score_gemma":0.006823903,"domain_scores_codex":[0.9995721,0.00008526324,0.0000354652,0.00008239633,0.0001642988,0.00006040681],"domain_scores_gemma":[0.999559,0.00008749075,0.00005112555,0.0001362819,0.00008149949,0.00008462628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006747511,0.0004789619,0.01282328,0.0007797586,0.0001673636,0.001715763,0.004260437,0.07359244,0.0658405,0.2274483,0.03032798,0.5818905],"study_design_scores_gemma":[0.000179193,0.0007344685,0.008072481,0.0002184754,0.0002500696,0.00220206,0.002365337,0.7182752,0.04581189,0.05594034,0.1656845,0.0002660307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08707843,0.0006598391,0.8819842,0.0008287947,0.00007793167,0.0003235377,0.0005492797,0.008783738,0.01971433],"genre_scores_gemma":[0.6414585,0.0007161675,0.3481342,0.0001807884,0.00008339285,0.000227232,0.0004998191,0.0002003624,0.008499474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00540069,"threshold_uncertainty_score":0.01073849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06533496640047837,"score_gpt":0.3448453181853626,"score_spread":0.2795103517848842,"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."}}