{"id":"W4412587948","doi":"10.3390/plants14152263","title":"High-Throughput DNA Extraction Using Robotic Automation (RoboCTAB) for Large-Scale Genotyping","year":2025,"lang":"en","type":"article","venue":"Plants","topic":"Chromosomal and Genetic Variations","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Genome Canada","keywords":"DNA extraction; Genotyping; Computer science; Scale (ratio); Scalability; Automation; Process engineering; Biology; Database; Engineering; Polymerase chain reaction; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001613732,0.00119089,0.00104879,0.001339665,0.0007331982,0.001283512,0.001656498,0.001154047,0.005765744],"category_scores_gemma":[0.002071314,0.0008893175,0.00116213,0.0006743913,0.0005920189,0.0006159266,0.001643294,0.001514448,0.00837976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000323753,"about_ca_system_score_gemma":0.0009506321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009452237,"about_ca_topic_score_gemma":0.002153122,"domain_scores_codex":[0.9977317,0.0003037495,0.0001838373,0.0006576619,0.000910303,0.0002125774],"domain_scores_gemma":[0.9985108,0.0003681973,0.0003421267,0.0003886514,0.0002653673,0.0001249709],"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":[0.0001718376,0.00007339216,0.0006337722,0.0007157321,0.00007400454,0.0002837192,0.0001692863,0.001055823,0.9266499,0.001367358,0.00476297,0.06404214],"study_design_scores_gemma":[0.00007048198,0.0004559863,0.004788935,0.0001748852,0.000120021,0.001627163,0.00009322834,0.01294247,0.8254,0.001756597,0.1523437,0.0002265491],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03967334,0.001514923,0.932937,0.0002937001,0.0002643965,0.001022118,0.002891868,0.01593092,0.005471729],"genre_scores_gemma":[0.07471074,0.001218338,0.9034261,0.0004850859,0.00008198102,0.002084224,0.007291547,0.001742131,0.008959793],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005765744,"threshold_uncertainty_score":0.0192883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02689258971985739,"score_gpt":0.2661305686359239,"score_spread":0.2392379789160665,"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."}}