{"id":"W6930132858","doi":"10.5281/zenodo.11193569","title":"Artificial intelligence and machine learning applications for cultured meat","year":2024,"lang":"en","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Agriculture Sustainability and Environmental Impact","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Relevance (law); Meat packing industry; Preprint; Support vector machine; Processed meat","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001501277,0.00079348,0.0005728541,0.003001796,0.0004040527,0.002237363,0.001059162,0.001118534,0.006228773],"category_scores_gemma":[0.005508068,0.0002465876,0.001003435,0.00407643,0.000492284,0.001798372,0.001260192,0.001308527,0.003188582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005889093,"about_ca_system_score_gemma":0.0005517903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001436301,"about_ca_topic_score_gemma":0.003081104,"domain_scores_codex":[0.9989706,0.000280573,0.00008972434,0.000271752,0.0003415982,0.00004589944],"domain_scores_gemma":[0.996874,0.001632998,0.0002439025,0.0006526131,0.0004960569,0.0001004767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004064028,0.0003511422,0.02922729,0.003508533,0.0004337279,0.0006909147,0.0003953344,0.04960796,0.01316503,0.02112302,0.1288553,0.7522352],"study_design_scores_gemma":[0.00005584144,0.000266473,0.05316273,0.001419497,0.0001731583,0.001358785,0.0008044493,0.2566059,0.02073258,0.1605226,0.5047059,0.0001921232],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1414758,0.06083006,0.5600256,0.01058594,0.002365315,0.0004850711,0.1353205,0.01429605,0.07461576],"genre_scores_gemma":[0.2738615,0.02708272,0.5095943,0.001496689,0.0009025128,0.0007709903,0.170049,0.00100995,0.01523231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006228773,"threshold_uncertainty_score":0.02083731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03982276305019744,"score_gpt":0.2552460643774975,"score_spread":0.2154233013273,"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."}}