{"id":"W6906482100","doi":"10.17605/osf.io/szneh","title":"Geodisy: geospatial discovery for Canadian research data","year":2019,"lang":"en","type":"article","venue":"OSF Preprints (OSF Preprints)","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Geospatial analysis; Data collection; Identification (biology); Key (lock)","routes":{"ca_aff":true,"ca_fund":false,"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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003487361,0.002093663,0.001405234,0.01289593,0.003721348,0.007234698,0.002975614,0.001205646,0.02561037],"category_scores_gemma":[0.02165635,0.001590986,0.00209826,0.01684265,0.001393622,0.004672992,0.006691605,0.002061808,0.01353567],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01091001,"about_ca_system_score_gemma":0.04249113,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8334455,"about_ca_topic_score_gemma":0.8680262,"domain_scores_codex":[0.9968176,0.0002105353,0.0002385118,0.0004798487,0.001695391,0.0005581285],"domain_scores_gemma":[0.9909589,0.001714272,0.0004635448,0.00249663,0.003200281,0.001166321],"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":[0.0003862012,0.00007637379,0.01199635,0.0009179214,0.0002937664,0.000302421,0.001281418,0.005599421,0.003471981,0.04130547,0.8331894,0.1011793],"study_design_scores_gemma":[0.000266856,0.00003219013,0.01536129,0.0003427377,0.0001473526,0.0002585658,0.001596266,0.04540096,0.00670038,0.03178695,0.8978328,0.0002736795],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.008182005,0.0008674174,0.08816652,0.002195345,0.0004017519,0.0006292784,0.7661401,0.1113518,0.02206587],"genre_scores_gemma":[0.03967862,0.001267825,0.1775923,0.0003389203,0.00009200029,0.0005005572,0.753963,0.01200435,0.01456235],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9927653,"threshold_uncertainty_score":0.3350706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07278484516444554,"score_gpt":0.3595914987398885,"score_spread":0.286806653575443,"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."}}