{"id":"W2035694260","doi":"10.1038/nmeth.1625","title":"High-throughput behavioral analysis in C. elegans","year":2011,"lang":"en","type":"article","venue":"Nature Methods","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":503,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Howard Hughes Medical Institute","keywords":"Caenorhabditis elegans; Habituation; Computer science; Throughput; Tracking (education); Caenorhabditis; Real-time computing; Artificial intelligence; Biology; Neuroscience; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006382162,0.0001997378,0.0002933353,0.0001701615,0.00004502077,0.00001645395,0.0003199777,0.0006341041,0.0001318486],"category_scores_gemma":[0.00006953445,0.0001924301,0.0001858985,0.0004377357,0.00007155396,0.000004049426,0.0001149834,0.0004325025,0.000006045122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000187086,"about_ca_system_score_gemma":0.00004022738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003406514,"about_ca_topic_score_gemma":0.000732878,"domain_scores_codex":[0.9985305,0.0002969838,0.0002438108,0.0004866266,0.0001320283,0.0003100612],"domain_scores_gemma":[0.999154,0.00001237391,0.00007578805,0.0005979961,0.00007743355,0.000082426],"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":[0.0001875811,0.0009419733,0.105863,0.00003773249,0.0009192062,0.0001093171,0.004109554,0.0004732272,0.8550836,0.001771817,0.001652123,0.02885083],"study_design_scores_gemma":[0.00062665,0.0003135584,0.1284492,0.000004682741,0.0006119999,0.00001855917,0.0001499572,0.0001271972,0.8565885,0.001521732,0.0110457,0.0005423416],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8462392,0.00137926,0.1507818,0.00005586201,0.0004795345,0.0001271037,0.00001419954,0.00002105753,0.0009019631],"genre_scores_gemma":[0.6624771,0.00006559902,0.336626,0.0002721889,0.0001162606,0.000007739116,0.00006244022,0.00001939934,0.000353213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1858442,"threshold_uncertainty_score":0.7847073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02688466522395872,"score_gpt":0.3600318584429669,"score_spread":0.3331471932190082,"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."}}