{"id":"W7010532651","doi":"","title":"Imagine Canadas Sector Monitor: Ongoing Effects of the COVID-19 Pandemic","year":2021,"lang":"en","type":"report","venue":"Issue Lab (Candid)","topic":"","field":"","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Government (linguistics); Public sector; Revenue; Coronavirus disease 2019 (COVID-19); Stock (firearms)","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":[],"category_scores_codex":[0.001439666,0.00106354,0.001936354,0.0004033831,0.000320161,0.0001277272,0.001293951,0.0007575537,0.001008192],"category_scores_gemma":[0.005451629,0.0008494087,0.0005917684,0.001397822,0.0003926282,0.0001178862,0.0006391859,0.001629,0.0002896087],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.007222328,"about_ca_system_score_gemma":0.01773955,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3457466,"about_ca_topic_score_gemma":0.1907092,"domain_scores_codex":[0.9925004,0.0008101981,0.001143899,0.001254715,0.00316988,0.001120854],"domain_scores_gemma":[0.9933915,0.0009616835,0.001548291,0.00249781,0.000868353,0.000732373],"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":[0.00005252253,0.0001450737,0.1135088,0.007601506,0.001167484,0.001409286,0.001035664,0.000116518,0.01754781,0.00001115713,0.8563852,0.001018971],"study_design_scores_gemma":[0.001474015,0.00006033781,0.007580062,0.001708707,0.0009948799,0.0007995851,0.00009180065,0.00002134744,0.007802249,0.00004037669,0.9784783,0.000948297],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4728076,0.2700247,0.0001087725,0.004419704,0.05822664,0.01408001,0.01694098,0.003578244,0.1598134],"genre_scores_gemma":[0.8987749,0.003652688,0.0001664509,0.001294924,0.007866833,0.0004401842,0.001108142,0.001643439,0.0850524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4259674,"threshold_uncertainty_score":0.999905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03816328544470798,"score_gpt":0.327428085904937,"score_spread":0.2892648004602291,"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."}}