{"id":"W7009422528","doi":"","title":"Electrovaya Named One of Canada's Clean Technology Winners in Deloitte's Technology Fast 50TM Program","year":2023,"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); Product (mathematics); Software","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":[0.0009956496,0.0006043375,0.0003801461,0.001088048,0.005919136,0.005236452,0.001107829,0.005957908,0.1297626],"category_scores_gemma":[0.001635417,0.0003917761,0.0005920487,0.0006200227,0.001010484,0.000901439,0.001631122,0.004220206,0.04314896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01573371,"about_ca_system_score_gemma":0.06854045,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8354393,"about_ca_topic_score_gemma":0.9562589,"domain_scores_codex":[0.9982738,0.00003398328,0.00001275603,0.00008304822,0.0008968741,0.0006994238],"domain_scores_gemma":[0.9978632,0.00006695925,0.00002123082,0.00005001509,0.001161231,0.0008373447],"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":[0.00001762426,0.00002781714,0.0003868279,0.00001427769,0.000002763141,0.00004851131,0.00002155037,0.00004994776,0.00015377,0.005825331,0.9844537,0.008997884],"study_design_scores_gemma":[0.00001089494,0.000007719791,0.001150354,0.0000170652,0.000002752004,0.00001572142,0.000102499,0.0001108898,0.0001648842,0.0004645428,0.9979457,0.000006818959],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.004462098,0.001046605,0.000708453,0.04304082,0.005885149,0.0002746943,0.005238809,0.000728768,0.9386146],"genre_scores_gemma":[0.002536543,0.0001330044,0.0001359035,0.003198665,0.00006032395,0.00001334404,0.0004422439,0.000060541,0.9934195],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1645607,"threshold_uncertainty_score":0.4340991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003264475743121627,"score_gpt":0.2019188655203442,"score_spread":0.1986543897772226,"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."}}