{"id":"W7006167494","doi":"","title":"State of learning in Canada no time for complacency : report on learning /","year":2015,"lang":"en","type":"other","venue":"Bibliothèque et Archives nationales du Québec (Québec government)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Human Resources and Skills Development Canada; United Nations Educational, Scientific and Cultural Organization; University of Alberta; Government of Ontario; University of Toronto; Strong; World Health Organization","keywords":"State (computer science); Government (linguistics); Experiential learning; Feature (linguistics)","routes":{"ca_aff":false,"ca_fund":true,"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":[0.002002138,0.0005384866,0.0005291213,0.003859725,0.006474321,0.005106725,0.002457257,0.001646228,0.03019019],"category_scores_gemma":[0.004339566,0.0005081265,0.0008073016,0.006320363,0.001386661,0.001071622,0.002468632,0.002167798,0.003805007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08125933,"about_ca_system_score_gemma":0.3399517,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.997829,"about_ca_topic_score_gemma":0.9988017,"domain_scores_codex":[0.9950282,0.0001024413,0.0001314414,0.0001796365,0.002358695,0.002199563],"domain_scores_gemma":[0.9856609,0.0003034092,0.0003925916,0.0001585607,0.007446997,0.006037544],"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":[0.0003690553,0.0004396658,0.1245768,0.0004300742,0.00007894408,0.0004059887,0.001827596,0.0008615289,0.0004837922,0.007232077,0.7533336,0.1099609],"study_design_scores_gemma":[0.00006180946,0.00006298943,0.5324708,0.0002914259,0.00004040969,0.0001003511,0.005961325,0.0008798911,0.0008734993,0.0003512913,0.4588161,0.00009000509],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.2372755,0.00646169,0.001237156,0.06444845,0.001724641,0.001017376,0.3585357,0.001939056,0.3273605],"genre_scores_gemma":[0.3344545,0.003845348,0.002138374,0.006435739,0.0001821814,0.0003535623,0.05580992,0.0003141965,0.5964662],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.08125933,"threshold_uncertainty_score":0.5895804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005499826691632241,"score_gpt":0.2225036339887037,"score_spread":0.2170038072970714,"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."}}