{"id":"W4398317917","doi":"10.7910/dvn/7jfrnl/t1tjs2","title":"product_process_patents_ctry_2_100_Canada.png","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Metallurgy and Material Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Product (mathematics); Process (computing); Business; Manufacturing engineering; Engineering; Computer science; Mathematics","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":[0.0006058346,0.00301329,0.001925852,0.007431016,0.001414196,0.004526764,0.003932571,0.002817055,0.2039224],"category_scores_gemma":[0.005211644,0.0009789538,0.001320151,0.01501159,0.0007274137,0.00199681,0.002201407,0.00194528,0.226412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006035785,"about_ca_system_score_gemma":0.008708338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2931894,"about_ca_topic_score_gemma":0.3981267,"domain_scores_codex":[0.9990757,0.00007271417,0.00006505568,0.0002458908,0.0003071165,0.000233482],"domain_scores_gemma":[0.9973568,0.0005033896,0.0002567441,0.0005448771,0.0009995839,0.0003387124],"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":[0.00002399335,0.000006897397,0.0002309806,0.0003092474,0.00001118435,0.000007393035,0.000006870736,0.0001100531,0.00003285748,0.0003977919,0.9979067,0.0009559934],"study_design_scores_gemma":[0.0000955206,0.000006132216,0.001396282,0.0002204541,0.00001711096,0.0000161862,0.00002971587,0.0002302028,0.0002332517,0.000847439,0.996887,0.00002062266],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003505147,0.00007214882,0.00001812186,0.00004670248,0.00001150459,0.0000031072,0.9985227,0.0002289987,0.001061741],"genre_scores_gemma":[0.0003296719,0.0001495059,0.0001180359,0.00005546199,0.000007277715,0.00002417593,0.9973387,0.0001141039,0.00186297],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7960776,"threshold_uncertainty_score":0.6821885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02625471927407848,"score_gpt":0.2493038823709335,"score_spread":0.223049163096855,"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."}}