{"id":"W6888960723","doi":"10.25318/2710000401-fra","title":"Paiements extra-muros en recherche et développement des entreprises, selon l'emplacement et le secteur de destinataires","year":2019,"lang":"fr","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Microinsurance; Limiting; Public sector; Term (time)","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":[0.001314274,0.001464822,0.001198851,0.006479435,0.001545885,0.002683735,0.002337413,0.001243207,0.03072778],"category_scores_gemma":[0.01143436,0.0006869297,0.0009734927,0.01764862,0.0005119189,0.001149232,0.001723497,0.001962159,0.02190244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01238111,"about_ca_system_score_gemma":0.03473412,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9076311,"about_ca_topic_score_gemma":0.941478,"domain_scores_codex":[0.9980579,0.0001674562,0.0002322861,0.0003453766,0.0006832579,0.0005136693],"domain_scores_gemma":[0.9901585,0.001533601,0.0009074046,0.0007520069,0.005649958,0.0009985045],"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":[0.00003606436,0.000006604364,0.002191793,0.0002869994,0.00001653107,0.00001218787,0.00004028638,0.000100986,0.00002166847,0.0004964595,0.9951806,0.001609856],"study_design_scores_gemma":[0.0001354217,0.000008125427,0.03407788,0.0005118595,0.00003648039,0.00003374959,0.0003234464,0.0002206837,0.0001884747,0.0004263434,0.964002,0.00003547643],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002031655,0.00009177976,0.00002209149,0.00009439985,0.00001612215,0.00000675026,0.9985468,0.00004856342,0.0009702976],"genre_scores_gemma":[0.001344943,0.0002063834,0.0002061467,0.00007342856,0.00001034491,0.00006514257,0.9943635,0.00003709511,0.003692988],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0923689,"threshold_uncertainty_score":0.1858257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06227246706132475,"score_gpt":0.3600452524465004,"score_spread":0.2977727853851756,"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."}}