{"id":"W7145227745","doi":"","title":"つながる人とデータ : IFLA WLIC 2013におけるLinked Dataとコミュニティ活動","year":2014,"lang":"ja","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nutrasource","funders":"","keywords":"Variety (cybernetics); Work (physics); Workflow; Information system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01779222,0.0007883001,0.0007862906,0.004921453,0.007411196,0.01657629,0.001785574,0.004785124,0.07045174],"category_scores_gemma":[0.01979216,0.0007907595,0.0009793567,0.005152789,0.003243559,0.005075585,0.005699378,0.006523058,0.06847247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01244435,"about_ca_system_score_gemma":0.02121922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08969977,"about_ca_topic_score_gemma":0.09593164,"domain_scores_codex":[0.9872485,0.001987613,0.001302085,0.001118773,0.006726419,0.001616597],"domain_scores_gemma":[0.984327,0.001843127,0.0008274587,0.001366452,0.0101323,0.001503702],"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":[0.0002013713,0.0001086446,0.002002156,0.0002875401,0.00002072728,0.0003525471,0.0009532167,0.0002488464,0.001743125,0.03039968,0.8390133,0.1246688],"study_design_scores_gemma":[0.00001141648,0.0000167965,0.001307029,0.0001588813,0.000007553012,0.00008070403,0.0001927579,0.00009646424,0.00135458,0.001144646,0.995594,0.00003514857],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0146654,0.01458888,0.02465582,0.09586134,0.01789823,0.001612069,0.02424175,0.005620099,0.8008565],"genre_scores_gemma":[0.07585171,0.01011652,0.0273186,0.04061258,0.00428932,0.002209622,0.03338071,0.003176551,0.8030444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08969977,"threshold_uncertainty_score":0.2356845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0212084569771399,"score_gpt":0.2398786009413462,"score_spread":0.2186701439642063,"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."}}