{"id":"W6893014833","doi":"10.5281/zenodo.13268484","title":"AI for Communications & Marketing","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canarie","funders":"","keywords":"Presentation (obstetrics); Marketing communication; Digital marketing; Key (lock)","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":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009708359,0.00009390334,0.00007837624,0.0002133749,0.002125699,0.002427996,0.003001556,0.00003935599,0.001595974],"category_scores_gemma":[0.0004947086,0.0001016925,0.00006335387,0.0006794009,0.00007713519,0.0007952008,0.002421453,0.0002752717,0.00570579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001334137,"about_ca_system_score_gemma":0.000006386632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007323282,"about_ca_topic_score_gemma":5.080846e-7,"domain_scores_codex":[0.9986999,0.0002753713,0.0002068352,0.000359215,0.0001973361,0.0002613631],"domain_scores_gemma":[0.9980627,0.0002178951,0.00004283853,0.001080013,0.0005001287,0.00009638762],"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":[0.00001192373,0.0000738165,5.655415e-7,0.0001087063,0.00004628192,0.000004721392,0.001541263,0.00003352917,0.001983822,0.1822845,0.5022769,0.3116339],"study_design_scores_gemma":[0.00008265836,0.00004873751,0.00004468279,0.00005772085,0.000006587436,0.00008050879,0.0001209814,0.07196173,0.000113833,0.001524931,0.9258544,0.000103244],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003261323,0.0003642945,0.8336011,0.03774395,0.0004806833,0.0005730287,0.0001337836,0.003484935,0.1232921],"genre_scores_gemma":[0.905898,0.0003239957,0.07945488,0.003424425,0.000593999,9.525753e-7,0.001661224,0.002894673,0.005747812],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9055719,"threshold_uncertainty_score":0.9993167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05028249110225466,"score_gpt":0.3055076636098774,"score_spread":0.2552251725076227,"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."}}