{"id":"W4282928539","doi":"10.5281/zenodo.6647198","title":"TECHNOLOGY FOR GROWING CANADIAN SAGE (CERCIS CANADENSIS) SEEDLINGS","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Turfgrass Adaptation and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"SAGE; Botany; Biology; Physics","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":[0.0005711058,0.0008137593,0.0002433746,0.001570295,0.001556309,0.0004708486,0.000729349,0.0004251804,0.005881035],"category_scores_gemma":[0.0003575266,0.0003434694,0.0005054366,0.001006077,0.0003703038,0.0003500663,0.000571218,0.0009763815,0.004203142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002246655,"about_ca_system_score_gemma":0.003290792,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1828144,"about_ca_topic_score_gemma":0.4538929,"domain_scores_codex":[0.9993292,0.00003522887,0.00002828568,0.00009699556,0.0004395832,0.00007076727],"domain_scores_gemma":[0.999656,0.00005064154,0.00003664232,0.00004783309,0.0001285303,0.00008036038],"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":[0.00007752892,0.00004549682,0.00125108,0.000198272,0.00001249634,0.0002134398,0.0001077586,0.000425683,0.9588056,0.0004458483,0.004634941,0.03378192],"study_design_scores_gemma":[0.0001155507,0.0003684484,0.0654512,0.0001189155,0.0001497672,0.002580991,0.0003896688,0.001914126,0.7332478,0.0005068943,0.1949998,0.0001568667],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3774397,0.01161711,0.4544956,0.004922308,0.001946817,0.005716815,0.01674458,0.006154801,0.1209623],"genre_scores_gemma":[0.301371,0.01356311,0.5809155,0.001136523,0.0002000361,0.003960792,0.01755389,0.001262761,0.08003637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8171856,"threshold_uncertainty_score":0.3635007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01706665205908062,"score_gpt":0.2047378761227381,"score_spread":0.1876712240636575,"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."}}