{"id":"W6945436455","doi":"10.25318/2710040001-fra","title":"Acquisition ou intégration des technologies de pointe, selon l'industrie et la taille de l'entreprise","year":2023,"lang":"fr","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Emerging technologies; Work (physics); Field (mathematics); Context (archaeology)","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":[0.001321425,0.00222745,0.001437695,0.00524255,0.001120878,0.002503719,0.002606005,0.002229197,0.02943764],"category_scores_gemma":[0.009221129,0.0006525612,0.001628931,0.01052157,0.0005655363,0.001831314,0.001553568,0.002318337,0.03690095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00542187,"about_ca_system_score_gemma":0.008879283,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4412572,"about_ca_topic_score_gemma":0.6041379,"domain_scores_codex":[0.9982273,0.0001834201,0.0001803912,0.0004151876,0.0006510208,0.0003426538],"domain_scores_gemma":[0.9951108,0.0009902809,0.0004102525,0.0007136135,0.002446693,0.0003284593],"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":[0.00005656854,0.00001785995,0.002114435,0.0005710789,0.00002946055,0.00001297071,0.00002964394,0.0003411897,0.00007602358,0.0008218376,0.9934522,0.0024768],"study_design_scores_gemma":[0.0001439688,0.00001164334,0.01740398,0.0004602559,0.00003700405,0.00003189386,0.0001895322,0.0007014743,0.0003551479,0.001293477,0.9793274,0.0000442769],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000150139,0.000068997,0.00005179063,0.00005968973,0.00002150573,0.000007056094,0.9988336,0.00008367233,0.0007235394],"genre_scores_gemma":[0.000558311,0.00008522027,0.000286072,0.00003050134,0.000007821828,0.00003854602,0.9977466,0.00002849973,0.001218334],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5587428,"threshold_uncertainty_score":0.8773776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0102813587802817,"score_gpt":0.2865112136963878,"score_spread":0.2762298549161061,"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."}}