{"id":"W74994008","doi":"10.1007/978-3-540-72108-6_14","title":"Mapping between dynamic ontologies in support of geospatial data integration for disaster management","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in geoinformation and cartography","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Centre de Géomatique du Québec","funders":"","keywords":"Geospatial analysis; Ontology; Computer science; Data science; Emergency management; Data integration; Domain (mathematical analysis); Semantic integration; Semantic heterogeneity; Ontology-based data integration; Information retrieval; Data mining; Geography; Semantic Web; Cartography; Semantic computing; Political science","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":[],"consensus_categories":[],"category_scores_codex":[0.001400655,0.0002564483,0.0004695328,0.001672775,0.0001692991,0.00006602153,0.0003202277,0.0003921464,0.00002537121],"category_scores_gemma":[0.00008486571,0.0002431269,0.0001179596,0.0003028748,0.0002772135,0.0006023063,0.0001478456,0.0002401844,0.000002952459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006361439,"about_ca_system_score_gemma":0.00003706596,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005324973,"about_ca_topic_score_gemma":0.0335907,"domain_scores_codex":[0.9979498,0.00002363146,0.001044909,0.0002260547,0.0004427237,0.000312897],"domain_scores_gemma":[0.9986352,0.0002614696,0.0005716422,0.0003314673,0.0001593955,0.00004082239],"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":[0.00008851013,0.00002131443,0.02093393,0.00150644,0.0003147281,0.000002524183,0.1578306,0.0001418738,6.979454e-7,0.06640014,0.0004163739,0.7523429],"study_design_scores_gemma":[0.004031471,0.0003694273,0.0616754,0.002090371,0.0002934315,0.000005043225,0.04222652,0.002066403,0.00001102356,0.09105258,0.7940251,0.002153238],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.002695361,0.000793195,0.4114603,0.0009858327,0.0009090126,0.00488689,0.0008790514,0.0001483436,0.577242],"genre_scores_gemma":[0.9828727,0.001970258,0.008397467,0.0005386383,0.0001860351,0.0001245219,0.004329138,0.00003479877,0.001546448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9801773,"threshold_uncertainty_score":0.9914429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05360884277567086,"score_gpt":0.3233640336038772,"score_spread":0.2697551908282064,"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."}}