{"id":"W4391935618","doi":"10.1007/978-3-031-47888-8_10","title":"SMEs Innovation Leveraged by Digital Transformation During Covid-19","year":2024,"lang":"en","type":"book-chapter","venue":"Springer proceedings in business and economics","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Business; Transformation (genetics); Digital transformation; Computer science; Medicine; World Wide Web; Internal medicine; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009377559,0.0002819322,0.0002097727,0.0009408023,0.0009475477,0.006203711,0.0005201173,0.001239132,0.0108771],"category_scores_gemma":[0.001546387,0.0001107963,0.0003708293,0.001549846,0.001246834,0.003301053,0.003438895,0.0009211334,0.00239992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002524249,"about_ca_system_score_gemma":0.001872736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00137124,"about_ca_topic_score_gemma":0.003659377,"domain_scores_codex":[0.9992508,0.000195522,0.00002584357,0.0000739503,0.0002161817,0.000237576],"domain_scores_gemma":[0.9992304,0.0002849894,0.00007943942,0.00009719327,0.0001269769,0.0001809822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003938069,0.0002829239,0.008164192,0.0005576879,0.00004694188,0.003707852,0.02198445,0.001959044,0.01681952,0.4316149,0.02993398,0.4845347],"study_design_scores_gemma":[0.00005320759,0.0006421186,0.03536353,0.001081096,0.00006677056,0.001677017,0.03289308,0.005567213,0.02136007,0.1032563,0.7979611,0.00007854158],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.229825,0.002967909,0.00360012,0.003215668,0.000290642,0.00005643204,0.00009265103,0.0001545317,0.7597972],"genre_scores_gemma":[0.9165111,0.001244113,0.001001588,0.0002656143,0.00009767846,0.00002987691,0.0001053283,0.00004123874,0.08070356],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0108771,"threshold_uncertainty_score":0.03638756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01708632318574919,"score_gpt":0.1979707146707287,"score_spread":0.1808843914849795,"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."}}