{"id":"W4398417365","doi":"10.7910/dvn/itijt0/xp05ol","title":"Technical_report_Canada_federal_2015.docx","year":2017,"lang":"zh","type":"dataset","venue":"Harvard Dataverse","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Natural language processing; Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008167719,0.0008129396,0.0008599998,0.0001247889,0.001162431,0.0008264366,0.002545156,0.0008266769,0.3133303],"category_scores_gemma":[0.0009026307,0.0008188134,0.0002783218,0.0001192471,0.001376785,0.001190872,0.003005384,0.0007202893,0.7598721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008130276,"about_ca_system_score_gemma":0.0002654139,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01831479,"about_ca_topic_score_gemma":0.007649238,"domain_scores_codex":[0.9949212,0.0001391133,0.0009728923,0.001542565,0.001435715,0.0009885133],"domain_scores_gemma":[0.9926589,0.00006925554,0.001319457,0.005249723,0.00003870308,0.0006639469],"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":[0.00007143218,0.00009309506,0.0009184562,0.0000750312,0.00007427376,0.0005593586,0.000005200942,0.000005531251,0.001315557,0.00003321083,0.9954516,0.001397269],"study_design_scores_gemma":[0.0004518816,0.0001048933,0.004216228,0.0001567231,0.0002893197,0.0004227707,0.0000221162,0.00001460083,0.0002752237,0.0002209596,0.9929209,0.0009043918],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003189169,0.000001118666,0.00002868674,0.00002511551,0.005988605,0.0007503393,0.9857641,0.000101902,0.004150958],"genre_scores_gemma":[0.004077535,0.000343388,0.0006261608,0.0003536818,0.0007827297,0.00005995515,0.9922951,0.00007491381,0.001386555],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4465419,"threshold_uncertainty_score":0.9994262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01598593897544923,"score_gpt":0.2513720792546248,"score_spread":0.2353861402791756,"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."}}