{"id":"W6894167100","doi":"10.5281/zenodo.8201929","title":"CREDIT CARDS CANADA","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Legal Cases and Commentary","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Credit card; Payment; Government (linguistics); Credit card interest; Debt","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007685818,0.00100329,0.0009054194,0.002305035,0.007876767,0.008952505,0.002006249,0.005459005,0.733967],"category_scores_gemma":[0.005780764,0.0009700579,0.0006461657,0.003489484,0.001088243,0.001879402,0.0017492,0.004262772,0.4040675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01425548,"about_ca_system_score_gemma":0.03486654,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7013389,"about_ca_topic_score_gemma":0.8572903,"domain_scores_codex":[0.998475,0.00007397785,0.00004124078,0.0002285941,0.00081483,0.00036642],"domain_scores_gemma":[0.9970384,0.000390145,0.00007069304,0.0002430944,0.001791727,0.0004659689],"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":[0.00002533669,0.00001332273,0.000124633,0.00002794507,0.000001687373,0.00003851101,0.00002980177,0.00004931452,0.00004154969,0.01056987,0.9766559,0.01242213],"study_design_scores_gemma":[0.00001247856,0.000003907043,0.000396219,0.00003676126,0.000001481181,0.00001176385,0.00008597463,0.00006952226,0.0000398805,0.001100964,0.9982349,0.000006251283],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000499007,0.0006776745,0.0001747847,0.004699445,0.000971791,0.00008737132,0.005852527,0.0002481294,0.9867893],"genre_scores_gemma":[0.0008277293,0.0001806989,0.00005410756,0.0006502473,0.00003108795,0.0000156414,0.0006452226,0.00005194925,0.9975433],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.733967,"threshold_uncertainty_score":0.60084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03336723609077415,"score_gpt":0.2530751109170676,"score_spread":0.2197078748262935,"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."}}