{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001816771,0.000221124,0.0004882441,0.000269703,0.00003713907,0.0000100933,0.0004470702,0.0001618193,0.2165073],"category_scores_gemma":[0.00001667386,0.0001972977,0.00003288728,0.0005418825,0.000190991,0.00002111474,0.0004477042,0.0001248614,0.00002227077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004990857,"about_ca_system_score_gemma":0.0001637496,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7583607,"about_ca_topic_score_gemma":0.8712937,"domain_scores_codex":[0.9986074,0.0000455854,0.0002804123,0.0002839868,0.0003429565,0.0004396534],"domain_scores_gemma":[0.9994951,0.00001270091,0.0001676812,0.0002752791,0.000003228215,0.0000460023],"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":[0.00002288729,0.0002061782,0.00732851,0.0001092669,0.0006948743,0.00005473588,0.0003230781,0.00005048286,0.0007485523,0.0007990861,0.966386,0.02327634],"study_design_scores_gemma":[0.0001663188,0.00003951972,0.001911001,0.000009411887,0.00007078203,0.000001949798,0.001774078,0.000001207847,0.002216442,0.00008607258,0.9934428,0.000280433],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.03833327,0.0001349072,0.000001851504,0.001200555,0.0001773517,0.001084313,0.00006003306,0.0002021943,0.9588055],"genre_scores_gemma":[0.01848658,0.00005637445,0.002276121,0.000269973,0.00001984452,0.0003780153,0.00007265202,0.0001711086,0.9782693],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2164851,"threshold_uncertainty_score":0.8045567,"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."}}