{"id":"W6947749497","doi":"10.4224/40002751","title":"Responding to Canada's needs: from COVID-19 to climate change: annual report 2019-2020","year":2020,"lang":"en","type":"report","venue":"NRC Digital Repository","topic":"Subterranean biodiversity and taxonomy","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Annual report; Climate change; Annual cycle; Work (physics)","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":[],"consensus_categories":[],"category_scores_codex":[0.004098602,0.001279939,0.0007033595,0.002861628,0.005004734,0.004822833,0.002705826,0.003281241,0.02721508],"category_scores_gemma":[0.00759393,0.0007558985,0.001362216,0.005653115,0.0008995544,0.001622409,0.004198999,0.002999494,0.007124218],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07996561,"about_ca_system_score_gemma":0.4955809,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9967456,"about_ca_topic_score_gemma":0.9976116,"domain_scores_codex":[0.9943382,0.0001421886,0.0001812364,0.0001370352,0.003466816,0.001734557],"domain_scores_gemma":[0.9846323,0.0003376924,0.0003132824,0.0001581972,0.010615,0.003943552],"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":[0.00007490312,0.00005219664,0.005773087,0.0004191147,0.00003579784,0.00007593827,0.000206841,0.0004278529,0.0001718859,0.001954321,0.9774299,0.01337828],"study_design_scores_gemma":[0.00008729335,0.00003964707,0.05919221,0.0008493644,0.00008256677,0.00004561671,0.001990895,0.0008260296,0.0008620027,0.00107424,0.9348702,0.00007995131],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.01668854,0.008050022,0.001159047,0.09123614,0.00493732,0.001118293,0.7246375,0.001465067,0.1507081],"genre_scores_gemma":[0.09668008,0.02119179,0.01393781,0.03880033,0.0006679174,0.002248836,0.4036758,0.0008079039,0.4219896],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9200344,"threshold_uncertainty_score":0.5801939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0508570424174691,"score_gpt":0.2371016538324266,"score_spread":0.1862446114149575,"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."}}