{"id":"W4304609475","doi":"10.22541/au.166549192.27137800/v1","title":"Turning the plant breeding phenotyping bottleneck into a pipeline","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Berry genetics and cultivation research","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Bottleneck; Phenomics; Plant breeding; Biology; Computer science; Biotechnology; Engineering; Genomics; Agronomy; Operations management; Genome","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.02211757,0.001676827,0.002336318,0.003271989,0.001736582,0.006680322,0.003502076,0.00213833,0.01448428],"category_scores_gemma":[0.02924211,0.001368467,0.0009600794,0.003103608,0.002601966,0.00745951,0.006996318,0.008353081,0.01079696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004125563,"about_ca_system_score_gemma":0.008278868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008357778,"about_ca_topic_score_gemma":0.01068675,"domain_scores_codex":[0.9917946,0.001916496,0.0003190372,0.001593255,0.003871138,0.0005054732],"domain_scores_gemma":[0.9515167,0.01525318,0.002477256,0.01034737,0.0152266,0.005178968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006342079,0.0002831757,0.008333537,0.00106465,0.000133741,0.0003882429,0.001356851,0.002832659,0.09010159,0.03299671,0.1554462,0.7064284],"study_design_scores_gemma":[0.0001441395,0.0005022931,0.02934987,0.001024803,0.000170813,0.00135148,0.00147589,0.01580295,0.04920818,0.1013362,0.7993686,0.000264778],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.04059922,0.009119214,0.8197348,0.06422354,0.002392345,0.0005278513,0.006424694,0.03394108,0.02303732],"genre_scores_gemma":[0.1067013,0.01388248,0.7876323,0.01768434,0.001826865,0.0009739896,0.01734007,0.01740921,0.03654946],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02211757,"threshold_uncertainty_score":0.1169703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07337784093347831,"score_gpt":0.2777609095528887,"score_spread":0.2043830686194104,"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."}}