{"id":"W6963142242","doi":"10.18164/c5c1db53-a17e-47e8-b499-7b05ce385640","title":"Montreal ACES mobile surveys 2023","year":2023,"lang":"en","type":"dataset","venue":"ECCC Data Catalogue","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"GDG Environnement; Government of Canada; Gouvernement du Québec; Environment and Climate Change Canada","funders":"","keywords":"Metropolitan area; Atmospheric composition; Global Positioning System; Air pollution; Atmospheric temperature; Weather station; Urban park","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":["metaepi_narrow","open_science"],"category_scores_codex":[0.00595065,0.001285744,0.001457749,0.000868724,0.0003162161,0.0004862214,0.01108011,0.001024816,0.0007054721],"category_scores_gemma":[0.001770254,0.00130137,0.0001922808,0.001805847,0.0004646627,0.001243753,0.009855436,0.001580129,0.6065231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002781435,"about_ca_system_score_gemma":0.0006268451,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3346747,"about_ca_topic_score_gemma":0.641067,"domain_scores_codex":[0.9914731,0.001317317,0.001112755,0.003102213,0.001447838,0.001546745],"domain_scores_gemma":[0.9825941,0.001008521,0.0007691021,0.01495078,0.00016598,0.0005114733],"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":[0.00002540436,0.0002427075,0.00001709899,0.0002559872,0.0004200274,0.000935236,0.0000151811,0.00002632903,0.000006490487,5.554627e-7,0.9973564,0.0006986265],"study_design_scores_gemma":[0.0005814083,0.00008817033,0.001666915,0.0001486107,0.0003737811,0.00005554339,0.00007190319,0.00005225965,0.000006751922,0.00002665924,0.9956151,0.001312883],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003088471,0.001118091,0.000004712099,0.00002950425,0.003186911,0.001256222,0.993442,0.0008623654,0.00006928833],"genre_scores_gemma":[0.000006511441,0.001273899,0.00003450823,0.00005697376,0.001646588,0.0004752413,0.9939849,0.000491834,0.00202949],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6058176,"threshold_uncertainty_score":0.9999894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0639355493086932,"score_gpt":0.3256767355293035,"score_spread":0.2617411862206103,"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."}}