{"id":"W2046932494","doi":"10.1016/j.jala.2008.12.010","title":"Rapid ID Technology (RIDT) in Plants: High-Speed DNA Fingerprinting in Grain Seeds for the Identification, Segregation, Purity, and Traceability of Varieties Using Labautomation Robotics","year":2009,"lang":"en","type":"article","venue":"JALA Journal of the Association for Laboratory Automation","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Traceability; DNA profiling; Consumables; Biotechnology; Phenomics; Identification (biology); Fingerprint (computing); DNA extraction; Biology; Computer science; Artificial intelligence; DNA; Genomics; Polymerase chain reaction; Genetics; Botany; Genome; Business","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":[0.002308693,0.0007811468,0.0007067483,0.001885487,0.00045882,0.001193183,0.001031166,0.001117069,0.007415878],"category_scores_gemma":[0.001504333,0.0007224841,0.0006546111,0.0009094143,0.0007467192,0.001108601,0.001332365,0.001545095,0.008978128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006929907,"about_ca_system_score_gemma":0.0008031757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001373027,"about_ca_topic_score_gemma":0.002467337,"domain_scores_codex":[0.9982452,0.0002964067,0.00008256979,0.000459963,0.0008116855,0.0001041026],"domain_scores_gemma":[0.998923,0.0003239051,0.000159163,0.0002567146,0.0002485635,0.00008863632],"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":[0.0001355702,0.00009140043,0.001248254,0.0003846579,0.00003161469,0.0001394222,0.0001332229,0.0003995875,0.8650238,0.002172722,0.004722031,0.1255177],"study_design_scores_gemma":[0.00009987856,0.0007733958,0.006829436,0.0001214685,0.00007774961,0.002747932,0.00009658929,0.01190846,0.8773178,0.001446025,0.09845544,0.0001258011],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05468382,0.004056776,0.9115587,0.0005885024,0.0004034578,0.0006901299,0.002111445,0.0118499,0.01405723],"genre_scores_gemma":[0.1234188,0.002665907,0.8430962,0.0005163722,0.00007413382,0.0005841078,0.00408799,0.0007582924,0.02479821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007415878,"threshold_uncertainty_score":0.02480865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01234885432873449,"score_gpt":0.2305795583236299,"score_spread":0.2182307039948955,"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."}}