{"id":"W7115417260","doi":"","title":"Optimizing Digital Marketing Performance in Pharma and Biopharma: case Alfa Laval","year":2025,"lang":"en","type":"other","venue":"Theseus (Ammattikorkeakoulujen)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Benchmarking; Competitor analysis; Leverage (statistics); Discoverability; Digital content; Visibility; Marketing research","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":[],"consensus_categories":[],"category_scores_codex":[0.00320958,0.0003408878,0.0002087376,0.001538424,0.004167392,0.006928034,0.001356539,0.002198934,0.00492266],"category_scores_gemma":[0.005288062,0.0002150181,0.000490998,0.001682891,0.001386364,0.003032255,0.002607233,0.001332804,0.001468169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009206343,"about_ca_system_score_gemma":0.005334085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03581129,"about_ca_topic_score_gemma":0.03593342,"domain_scores_codex":[0.9962037,0.001599578,0.0000918257,0.0002905039,0.0008338978,0.0009804582],"domain_scores_gemma":[0.9938052,0.002385474,0.0004947977,0.0003110328,0.001073882,0.001929514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004011505,0.01236929,0.1667383,0.001694707,0.000206847,0.06277815,0.05610464,0.02825561,0.01922306,0.1467488,0.1012351,0.400634],"study_design_scores_gemma":[0.0004982586,0.004808906,0.1433069,0.0009822666,0.0001849028,0.009413248,0.1749814,0.06290895,0.01975377,0.01170403,0.5710846,0.0003728366],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.9281929,0.0007765195,0.0007886959,0.004329506,0.00004879146,0.0001871296,0.0001495933,0.0001014378,0.06542539],"genre_scores_gemma":[0.9828374,0.0004577128,0.001553426,0.0005061309,0.0000328951,0.00004124258,0.0001421296,0.00002722632,0.01440167],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03581129,"threshold_uncertainty_score":0.07120574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01601789413167783,"score_gpt":0.2632506969100293,"score_spread":0.2472328027783515,"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."}}