{"id":"W7100897108","doi":"","title":"COLUMNS Member A-LIRT........................p.4 Tech Talk.................................p.11 Inside From The Vice President Looking Forward to Toronto","year":2015,"lang":"en","type":"article","venue":"","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vice president; High tech; Work (physics); Information technology; Closing (real estate)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008665725,0.0005512909,0.0004380731,0.0008093578,0.003387442,0.004205558,0.0007541605,0.002637095,0.5793789],"category_scores_gemma":[0.003044714,0.0004046458,0.0003841976,0.0006795559,0.0005855117,0.0018709,0.001444878,0.00404145,0.3230175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003606986,"about_ca_system_score_gemma":0.004376573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02859668,"about_ca_topic_score_gemma":0.09428217,"domain_scores_codex":[0.9992622,0.00005547999,0.00002257183,0.000118934,0.0003749118,0.0001659777],"domain_scores_gemma":[0.9947224,0.0003177494,0.0001517305,0.00009531817,0.002518004,0.002194784],"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":[0.000009359575,0.000005562387,0.0000672563,0.00001771157,3.298261e-7,0.00001790827,0.00002072959,0.000005991042,0.0001130678,0.0001704593,0.9946495,0.004922204],"study_design_scores_gemma":[0.000002965544,0.000009781634,0.0004720772,0.00003243921,8.156093e-7,0.00003143608,0.000176096,0.00002618108,0.00009017681,0.00006292535,0.9990913,0.00000376952],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001437209,0.005366028,0.0009448551,0.1864875,0.1107248,0.0004677028,0.002420167,0.001549575,0.6906024],"genre_scores_gemma":[0.001385675,0.000499321,0.00006011547,0.003881001,0.002853096,0.00002657933,0.0001531422,0.0001034587,0.9910376],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4206211,"threshold_uncertainty_score":0.5999649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03042940077923668,"score_gpt":0.3060256955424969,"score_spread":0.2755962947632603,"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."}}