{"id":"W1999960371","doi":"10.1016/j.mimet.2011.01.028","title":"High-throughput screening of microbial adaptation to environmental stress","year":2011,"lang":"en","type":"article","venue":"Journal of Microbiological Methods","topic":"Legume Nitrogen Fixing Symbiosis","field":"Agricultural and Biological Sciences","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Université de Sherbrooke; Université Laval","keywords":"Rhizobium leguminosarum; Biology; Bacteria; Rhizobium; Microbiology; Escherichia coli; Antimicrobial; High-throughput screening; Microorganism; Strain (injury); Symbiosis; Rhizobiaceae; Genetics; Gene","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":[0.0004125235,0.0006425296,0.0008173364,0.0004645401,0.0003015622,0.0006499899,0.0004250782,0.0005059914,0.000761365],"category_scores_gemma":[0.000413297,0.0002437266,0.0005041751,0.0005640961,0.0001309431,0.000245486,0.0004830712,0.0005547104,0.000484479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001990136,"about_ca_system_score_gemma":0.0002257835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006005584,"about_ca_topic_score_gemma":0.00141416,"domain_scores_codex":[0.9994977,0.000110151,0.00002916297,0.00007672495,0.000216541,0.00006976838],"domain_scores_gemma":[0.9998137,0.00007684413,0.00002450508,0.00002412277,0.00003615153,0.0000246047],"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":[0.00009809605,0.0001328065,0.0004277472,0.00005042796,0.00001652672,0.00002027642,0.00001245436,0.0001393672,0.9952747,0.00001187544,0.00009005354,0.003725725],"study_design_scores_gemma":[0.00003290706,0.00100134,0.01391265,0.0000087395,0.00008829108,0.0001795898,0.00006400962,0.002816588,0.9796878,0.00005609323,0.002129781,0.00002214431],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9792449,0.00178002,0.01347394,0.0002371012,0.00006853876,0.0002329923,0.003230553,0.0002857277,0.001446235],"genre_scores_gemma":[0.9595551,0.002073538,0.0267944,0.0001578932,0.00004518602,0.0003832795,0.006554839,0.00006513586,0.004370651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008173364,"threshold_uncertainty_score":0.002547026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06010472115193046,"score_gpt":0.274078886385919,"score_spread":0.2139741652339886,"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."}}