{"id":"W6992750864","doi":"","title":"Meta Materials Named One of Canada's Clean Technology Winners in Deloitte's Fast 50 Program","year":2022,"lang":"en","type":"other","venue":"","topic":"Sustainable Development and Policies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Clean technology; Key (lock); Identification (biology); Window (computing); Automation","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":[0.00115291,0.0006853581,0.000497821,0.001618903,0.006909777,0.007294217,0.001454221,0.00475652,0.2718509],"category_scores_gemma":[0.002336045,0.0004253255,0.0005965025,0.001200404,0.001043807,0.001396307,0.002086257,0.002983629,0.08473951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02002303,"about_ca_system_score_gemma":0.06082788,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7352995,"about_ca_topic_score_gemma":0.9301467,"domain_scores_codex":[0.9981008,0.00005169039,0.00001365454,0.00008345776,0.001197043,0.0005532672],"domain_scores_gemma":[0.9975923,0.0001101335,0.00002859567,0.0001074758,0.001249416,0.000912153],"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":[0.00001708,0.00002144249,0.0001184665,0.0000199519,0.000002042009,0.00002884247,0.00002981795,0.00004480872,0.0001762829,0.009876688,0.9793943,0.0102703],"study_design_scores_gemma":[0.000007646606,0.000004602686,0.00035669,0.00002122391,0.000001925247,0.000007577684,0.00007345661,0.00006496647,0.0001644149,0.0007197067,0.9985719,0.000005944129],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001432521,0.0006057917,0.0006036976,0.01136792,0.003325683,0.0002144624,0.005170744,0.0006468033,0.9766324],"genre_scores_gemma":[0.001784442,0.0001300592,0.0002443477,0.001667532,0.00007509616,0.00002200859,0.0005725492,0.0001078897,0.9953961],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2718509,"threshold_uncertainty_score":0.9094318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009574258416324598,"score_gpt":0.207690935174368,"score_spread":0.1981166767580434,"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."}}