{"id":"W6912966256","doi":"10.5683/sp3/pgaiv7","title":"GTA Bike Surveys - Summer 2022 - Calibrated data","year":2023,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Analyser; Calibration; Weather station; Automatic weather station; Summer season","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004889115,0.0009935539,0.001124908,0.0008476591,0.0002174581,0.0003929943,0.006514772,0.0009379003,0.001176634],"category_scores_gemma":[0.001779924,0.0009770636,0.0001572483,0.002419302,0.0002803605,0.0005876924,0.00414008,0.001189696,0.03875331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002031616,"about_ca_system_score_gemma":0.000755253,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8585975,"about_ca_topic_score_gemma":0.7450182,"domain_scores_codex":[0.9918194,0.002402946,0.0009452208,0.002085285,0.001551428,0.001195663],"domain_scores_gemma":[0.9867812,0.000545733,0.0006182418,0.01138823,0.0002005417,0.0004660028],"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":[0.00002976659,0.0001369785,0.0000742777,0.0001247422,0.0004515917,0.0006948535,0.000004978174,0.000003197138,0.00001276833,0.000006100709,0.9983431,0.0001176752],"study_design_scores_gemma":[0.0005039627,0.00003409633,0.003839634,0.0001016523,0.0004083733,0.00001158255,0.00001577961,0.000120228,0.000008121277,0.00002782871,0.9938446,0.001084192],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00000151988,0.0002829597,0.000004883121,0.0001454792,0.0008797082,0.0006444378,0.9967873,0.0009513478,0.0003024178],"genre_scores_gemma":[2.502292e-7,0.000642517,0.00001711765,0.0002396052,0.0008681288,0.0000934726,0.9962298,0.0006483737,0.001260756],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1135793,"threshold_uncertainty_score":0.9997364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1210159751688245,"score_gpt":0.3431502836367218,"score_spread":0.2221343084678974,"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."}}