{"id":"W4385064779","doi":"10.1371/journal.pone.0287739","title":"Accelerated high-throughput imaging and phenotyping system for small organisms","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Gordon and Betty Moore Foundation","keywords":"Throughput; Automation; Computer science; Biology; Data collection; Data science; Computational biology; Biochemical engineering; Statistics; Telecommunications; Engineering","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":[0.001836896,0.0008915106,0.001500391,0.0008727322,0.0007678014,0.0009752431,0.001727651,0.001013254,0.007450808],"category_scores_gemma":[0.002248129,0.0007448267,0.0009713179,0.0007061606,0.0005400255,0.001342503,0.001654297,0.001970949,0.004480859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006266327,"about_ca_system_score_gemma":0.0007446213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001227099,"about_ca_topic_score_gemma":0.002513558,"domain_scores_codex":[0.9987341,0.0001887072,0.00008279509,0.0004489862,0.0004554465,0.00009016804],"domain_scores_gemma":[0.997106,0.0009691846,0.0002418254,0.0008974029,0.0004793147,0.0003064032],"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":[0.0003586498,0.0001889525,0.002965519,0.0002501105,0.0001181082,0.0001494887,0.0001699045,0.001485052,0.9464018,0.001398781,0.009966566,0.03654699],"study_design_scores_gemma":[0.0001660331,0.0006021202,0.02739595,0.00006997942,0.0001519548,0.0006653166,0.00009408528,0.0835984,0.8001198,0.002980832,0.08386482,0.0002907864],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06219789,0.0003147102,0.8695924,0.0004445498,0.0002255387,0.0006580062,0.00798674,0.05606822,0.002511966],"genre_scores_gemma":[0.1077309,0.0003594653,0.8728824,0.0004571212,0.000107031,0.003319122,0.007611607,0.002788889,0.004743512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007450808,"threshold_uncertainty_score":0.02492547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05769264033431209,"score_gpt":0.219357665599029,"score_spread":0.1616650252647169,"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."}}