{"id":"W3204210642","doi":"10.1101/2021.09.27.460959","title":"OnePetri: accelerating common bacteriophage Petri dish assays with computer vision","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; AbbVie Canada; Howard Hughes Medical Institute","keywords":"Petri dish; Enumeration; Bacteriophage; Computer science; Petri net; Artificial intelligence; Mathematics; Algorithm; Discrete mathematics; Biology; Microbiology","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":[0.002127141,0.001912051,0.001160118,0.002012578,0.0003939363,0.001652309,0.003072013,0.001248678,0.01097135],"category_scores_gemma":[0.003616582,0.000894334,0.001189072,0.0007685203,0.0004047476,0.001223195,0.001627313,0.001711652,0.006450226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001086567,"about_ca_system_score_gemma":0.001152055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002840027,"about_ca_topic_score_gemma":0.00486163,"domain_scores_codex":[0.9985453,0.0001501787,0.00006993928,0.000411732,0.0006917908,0.0001310244],"domain_scores_gemma":[0.9981924,0.0006778941,0.0002186718,0.0003141357,0.0004758531,0.0001209869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005569079,0.0004536284,0.005083727,0.000884945,0.0002553489,0.0001551951,0.0001966264,0.03303007,0.238471,0.003707021,0.06139366,0.655812],"study_design_scores_gemma":[0.00004215453,0.0002241351,0.003735685,0.00005573607,0.00005065968,0.0002333677,0.00003885809,0.7860002,0.1878173,0.003524659,0.01816924,0.0001080316],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02065106,0.0004734558,0.8855862,0.0001978586,0.0001757785,0.0002813506,0.001687704,0.08837121,0.002575275],"genre_scores_gemma":[0.08050402,0.0004049131,0.9052264,0.0002260808,0.00005934099,0.0006704555,0.003438231,0.003236931,0.006233597],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01097135,"threshold_uncertainty_score":0.03670275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008045630281057647,"score_gpt":0.2282173646005251,"score_spread":0.2201717343194674,"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."}}