{"id":"W2119342649","doi":"10.1002/asi.23347","title":"A lead‐lag analysis of the topic evolution patterns for preprints and publications","year":2015,"lang":"en","type":"article","venue":"Journal of the Association for Information Science and Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"National Science Foundation","keywords":"Popularity; Lag; Time lag; Computer science; Regression analysis; Data science; Information retrieval; Machine learning; Psychology","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"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.02367124,0.00003769198,0.0001521491,0.01845024,0.0003038283,0.0004701195,0.001260012,0.00007817177,0.000002260688],"category_scores_gemma":[0.1109579,0.00001929707,0.00008935457,0.06956653,0.0001965245,0.001793982,0.0003269003,0.00009367007,0.00000114823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002902455,"about_ca_system_score_gemma":0.0004235247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008580152,"about_ca_topic_score_gemma":0.00001363783,"domain_scores_codex":[0.9963993,0.00003613663,0.0005918439,0.0000940173,0.002730888,0.0001478395],"domain_scores_gemma":[0.9840121,0.0007814564,0.001557103,0.0002825036,0.01330668,0.00006016788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005717895,0.00001701447,0.9501926,0.00000261484,0.00004257706,2.879178e-9,0.0003384429,0.00008004624,0.0002677679,0.02437589,0.001750009,0.02292737],"study_design_scores_gemma":[0.0005159273,0.00007640044,0.9168852,0.000004586324,0.00007882046,0.000003670385,0.00184046,0.01928165,0.001557082,0.0350601,0.02465123,0.00004486224],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9582981,0.00003961107,0.0212248,0.01909209,0.0003800825,0.0003422968,0.00004863117,0.000004981594,0.0005694286],"genre_scores_gemma":[0.9991538,0.00001489531,0.0004015188,0.00006399902,0.000009447625,0.000007870693,3.921214e-7,6.797078e-7,0.0003474159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08728664,"threshold_uncertainty_score":0.9926748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3331102629529904,"score_gpt":0.5118160362094799,"score_spread":0.1787057732564895,"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."}}