{"id":"W2124417984","doi":"10.5194/isprsarchives-xl-2-w1-53-2013","title":"TOWARDS A COLLABORATIVE KNOWLEDGE DISCOVERY SYSTEM FOR ENRICHING SEMANTIC INFORMATION ABOUT RISKS OF GEOSPATIAL DATA","year":2013,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Université Laval; Centre de Géomatique du Québec","funders":"","keywords":"Geospatial analysis; Computer science; Data science; Knowledge extraction; Context (archaeology); Domain knowledge; Scope (computer science); Semantic Web; Knowledge management; Risk analysis (engineering); World Wide Web; Data mining; Geography; Business","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001925564,0.0004467064,0.0005770713,0.001008135,0.001266913,0.001636862,0.00386462,0.0001115531,0.000002448256],"category_scores_gemma":[0.001304211,0.0002773544,0.0003288458,0.001216047,0.002266644,0.002594305,0.00231698,0.0003555701,0.000003725716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005755942,"about_ca_system_score_gemma":0.0005271434,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7866227,"about_ca_topic_score_gemma":0.1282825,"domain_scores_codex":[0.9950575,0.0003468678,0.001898391,0.0004537874,0.001693214,0.000550252],"domain_scores_gemma":[0.9940658,0.001608135,0.00254744,0.0009068369,0.0007347518,0.000137027],"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":[0.00009322652,0.00002224205,0.0001538341,0.0001392582,0.0001170852,1.185469e-7,0.006727345,0.001776905,0.0009046681,0.00009503568,0.00008181034,0.9898885],"study_design_scores_gemma":[0.0008828972,0.0001711526,0.004810573,0.0005249656,0.00005317909,0.00007474252,0.004556694,0.9750096,0.006365453,0.005118595,0.002118885,0.0003132216],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008646498,0.00006176417,0.9791309,0.003201246,0.002516327,0.001254754,0.0002444763,0.00006781703,0.004876201],"genre_scores_gemma":[0.9848356,0.0001261406,0.0142612,0.0005287367,0.0001275073,0.000001402209,0.00007644047,0.000008728755,0.00003420241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9895753,"threshold_uncertainty_score":0.9999679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03786780550816921,"score_gpt":0.2981521450451878,"score_spread":0.2602843395370186,"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."}}