{"id":"W2896552281","doi":"10.3390/publications6040041","title":"Planning for Academic Publishing after Retirement","year":2018,"lang":"en","type":"article","venue":"Publications","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Anticipation (artificial intelligence); Publishing; Publication; Research Assessment Exercise; Public relations; Mandatory retirement; Political science; Labour economics; Management; Sociology; Business; Economics; Law; Higher education; Computer science","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":["metaresearch","bibliometrics","scholarly_communication","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.01733102,0.00008389077,0.0001235884,0.02934506,0.0004599892,0.007628616,0.002489309,0.0001236481,0.001074673],"category_scores_gemma":[0.09851095,0.00006253459,0.00008648232,0.0994497,0.0001661417,0.002826997,0.0005071962,0.0002461039,0.0004326876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009636036,"about_ca_system_score_gemma":0.000186858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001076377,"about_ca_topic_score_gemma":0.000005377557,"domain_scores_codex":[0.9942849,0.00006644557,0.0005380854,0.0005880544,0.003982437,0.0005400852],"domain_scores_gemma":[0.9887775,0.002851789,0.0002064094,0.0008471866,0.006925455,0.0003916466],"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":[0.00001555455,0.00004859895,0.1780275,0.000002307828,0.00000927893,2.534562e-7,0.0003908781,0.000002339474,0.0002472922,0.02018286,0.7142693,0.08680387],"study_design_scores_gemma":[0.0001558273,0.00004756662,0.2404674,0.000003894714,0.000002651864,0.000001275625,0.0001665117,0.0034292,0.0002438262,0.02032807,0.7350634,0.00009029492],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3423231,0.001426711,0.3376733,0.1541293,0.002108735,0.001666774,0.0003163968,0.000281833,0.1600738],"genre_scores_gemma":[0.9838633,0.00001090417,0.00562306,0.0009086087,0.0005351238,0.0003352392,0.00001795765,0.000009893883,0.008695898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6415402,"threshold_uncertainty_score":0.9998385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8246445626788835,"score_gpt":0.6557962135259534,"score_spread":0.1688483491529301,"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."}}