{"id":"W6912212906","doi":"10.5281/zenodo.3662848","title":"Federated Geospatial Data Discovery for Canada","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canarie","keywords":"Geospatial analysis; Software; Data discovery; Interoperability; Identification (biology); Knowledge extraction","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.007706152,0.001554035,0.001154276,0.009667587,0.003971845,0.008071362,0.005953436,0.001895041,0.04091229],"category_scores_gemma":[0.01963062,0.0009964363,0.001153785,0.01508304,0.001466902,0.004863359,0.004181132,0.002073699,0.02576666],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03211026,"about_ca_system_score_gemma":0.1054032,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9670804,"about_ca_topic_score_gemma":0.9675941,"domain_scores_codex":[0.9953773,0.0001814962,0.0002557797,0.0003203912,0.003303309,0.0005615959],"domain_scores_gemma":[0.9587584,0.002430537,0.000571561,0.003254187,0.03199049,0.002994766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001122063,0.00004394901,0.001554445,0.0001292497,0.00003522872,0.00007472885,0.0001077827,0.001164746,0.000548406,0.006639097,0.9504375,0.03915255],"study_design_scores_gemma":[0.0000598016,0.00001093103,0.003781983,0.0001922289,0.00004305606,0.00005413033,0.0001668824,0.0034301,0.001628583,0.002587505,0.9879801,0.000064705],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.012823,0.006701943,0.05363145,0.03832293,0.004572144,0.0008733708,0.7255637,0.05093654,0.1065749],"genre_scores_gemma":[0.02633469,0.005297677,0.1064101,0.003238979,0.0004911864,0.0005090581,0.6958216,0.005240033,0.1566567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9678897,"threshold_uncertainty_score":0.2329773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07980012960608882,"score_gpt":0.2750559250652891,"score_spread":0.1952557954592003,"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."}}