{"id":"W4303411333","doi":"10.2144/btn-2022-0102","title":"Biohacking the Food Chain: Using CRISPR to Combat the Global Food Crisis","year":2022,"lang":"en","type":"article","venue":"BioTechniques","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Oncolytics Biotech (Canada)","funders":"","keywords":"CRISPR; Food waste; Global population; Population; Climate change; Key (lock); Business; Food chain; Biotechnology; Natural resource economics; Biology; Ecology; Environmental health; Economics; Gene; Genetics; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003156887,0.000157775,0.000102024,0.00002796026,0.0004255134,0.000048315,0.0005006409,0.00008096901,0.00001421582],"category_scores_gemma":[0.00002860035,0.0001107777,0.0001079325,0.0002434395,0.00003802298,0.000001775779,0.000644257,0.0001502415,9.88383e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005793464,"about_ca_system_score_gemma":0.00003479454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000767684,"about_ca_topic_score_gemma":0.00004390621,"domain_scores_codex":[0.9990252,0.00008327325,0.0001593201,0.0002827062,0.0001801249,0.0002693275],"domain_scores_gemma":[0.9993271,0.000009230262,0.00004260974,0.0005351041,0.00003418904,0.00005182023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001170838,0.0001416423,0.001196572,0.00004219828,0.0003058909,0.000005593661,0.0007377065,0.00647854,0.9329674,0.005165622,0.0413982,0.01144356],"study_design_scores_gemma":[0.0001168496,0.0009493668,0.0003697741,0.000007573491,0.00002840865,0.00005789801,0.0008115052,0.0003198862,0.7833568,0.0005223218,0.2131876,0.0002719866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7437295,0.004149362,0.2456066,0.004584264,0.0004201635,0.0008254021,0.0001661348,0.000152487,0.0003660733],"genre_scores_gemma":[0.9952328,0.00005345068,0.002550484,0.001822456,0.0002003091,0.00009011123,0.00001299635,0.00002268024,0.00001476375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2515033,"threshold_uncertainty_score":0.4517383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01852342215723277,"score_gpt":0.3160576890853818,"score_spread":0.2975342669281491,"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."}}