{"id":"W4399858053","doi":"10.46399/muhendismakina.1362765","title":"THE IMPACT OF COVID‐19 ON THE TECHNOLOGY SECTOR: THE CASE OF THE TURKISH CONSULTANCY COMPANY","year":2024,"lang":"en","type":"article","venue":"Mühendis ve Makina","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Coronavirus disease 2019 (COVID-19); Pandemic; Turkish; Business; Marketing; Regression analysis; Econometrics; Economics; Computer science; Machine learning; Geography; Medicine","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":[0.001932587,0.0004951207,0.0002978122,0.001537167,0.00343137,0.002801565,0.0009988853,0.00239697,0.002423201],"category_scores_gemma":[0.005683185,0.0002669503,0.0005692884,0.001653105,0.001597781,0.001785411,0.001543704,0.002301028,0.0003688381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007244901,"about_ca_system_score_gemma":0.00257164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1276606,"about_ca_topic_score_gemma":0.1185917,"domain_scores_codex":[0.9982089,0.0007399288,0.0000546762,0.0001230241,0.0003432801,0.0005302859],"domain_scores_gemma":[0.9934779,0.004192614,0.0007446483,0.0001678078,0.0006691132,0.0007477942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009729489,0.00183245,0.5724502,0.0005488957,0.0002473794,0.1022843,0.03509221,0.1550134,0.005068445,0.03128402,0.02072384,0.07448196],"study_design_scores_gemma":[0.000110415,0.0008279809,0.4288739,0.0005333083,0.0001949387,0.007837738,0.1740427,0.3347562,0.00465718,0.007298354,0.0405019,0.0003654787],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906358,0.0003024781,0.0003377832,0.002053028,0.0000141564,0.00001995364,0.0001425283,0.00001611063,0.006478081],"genre_scores_gemma":[0.9980084,0.0002599889,0.0002892579,0.000124267,0.00001353977,0.00000712995,0.00009097096,0.000007276208,0.001199176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1276606,"threshold_uncertainty_score":0.253835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05218620363603781,"score_gpt":0.3051212012900211,"score_spread":0.2529349976539833,"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."}}