{"id":"W4220692319","doi":"10.1016/j.inpa.2022.03.004","title":"Fusion of spatiotemporal and thematic features of textual data for animal disease surveillance","year":2022,"lang":"en","type":"article","venue":"Information Processing in Agriculture","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"European Regional Development Fund; Région Occitanie Pyrénées-Méditerranée; Centre de Coopération Internationale en Recherche Agronomique pour le Développement; Agence Nationale de la Recherche; European Commission","keywords":"Context (archaeology); Computer science; Representation (politics); Information retrieval; Selection (genetic algorithm); Feature selection; Disease surveillance; Feature (linguistics); Sensor fusion; Data mining; Artificial intelligence; Natural language processing; Disease; Geography; Medicine; Pathology; Linguistics","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":[0.0003594617,0.0001001232,0.0002462569,0.00009288497,0.00007711739,0.00001865847,0.0001827148,0.00003144516,0.00001633894],"category_scores_gemma":[0.0003700218,0.00007165923,0.00002767444,0.0003155836,0.00004827459,0.0006203913,0.0001992737,0.0001115663,3.507705e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002911517,"about_ca_system_score_gemma":0.0001434388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001892394,"about_ca_topic_score_gemma":0.00001626597,"domain_scores_codex":[0.9989515,0.00003337082,0.0004355277,0.0001300369,0.0003426784,0.0001068807],"domain_scores_gemma":[0.9990427,0.00004217101,0.0004104313,0.0002576053,0.0001864648,0.00006060857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01049958,0.001915126,0.5755157,0.03935232,0.0001489522,0.00002069003,0.01271816,0.001788406,0.01265987,0.00236711,0.1030727,0.2399414],"study_design_scores_gemma":[0.001794173,0.0001720501,0.9822679,0.000340294,0.00003569681,0.00002208974,0.002357069,0.006483058,0.000187699,0.0000805881,0.006100436,0.0001589503],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875855,0.001779319,0.0005917528,0.001069841,0.00006052907,0.001399974,0.006648684,0.0000698942,0.0007944951],"genre_scores_gemma":[0.9922975,0.00001542143,0.001189629,0.0001063415,0.0000211394,0.00004032557,0.006294807,0.000004549041,0.00003029906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4067522,"threshold_uncertainty_score":0.2922179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01658669233441785,"score_gpt":0.2756903477295713,"score_spread":0.2591036553951535,"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."}}