{"id":"W2588383024","doi":"10.29173/cais473","title":"Understanding How Experts Use Bioinformatics Resources","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Data science; Computer science; Resource (disambiguation); Software; Bioinformatics; Data mining; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003992297,0.0002355165,0.0003021443,0.000129468,0.0001297237,0.002267321,0.001199078,0.0002520212,0.00002298064],"category_scores_gemma":[0.01211614,0.0001658942,0.0001636001,0.0002101647,0.0007469712,0.0006767627,0.0007530521,0.0001660117,0.000003739673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003746797,"about_ca_system_score_gemma":0.0001551476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007866937,"about_ca_topic_score_gemma":0.000009762594,"domain_scores_codex":[0.9982734,0.00001750578,0.0004552903,0.0002072713,0.0005658874,0.0004805939],"domain_scores_gemma":[0.9852029,0.00006143086,0.0004676138,0.0002412171,0.01381447,0.000212384],"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":[0.0003166416,0.0003441517,0.1519918,0.001626684,0.0004734724,0.000001115933,0.05007022,0.000007572273,0.6389635,0.002803288,0.1398551,0.01354645],"study_design_scores_gemma":[0.002137752,0.002128465,0.05555755,0.000719226,0.0001257179,0.00007117011,0.05553927,0.004133567,0.5751373,0.003988412,0.2992169,0.001244723],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925056,0.0000820895,0.0003966066,0.002095434,0.0001014754,0.0003870793,0.00007241609,0.00001466836,0.004344692],"genre_scores_gemma":[0.9963785,0.0003699466,0.001695712,0.0002544562,0.0001023462,0.00002031991,0.00001325362,0.00001847141,0.001146971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1593618,"threshold_uncertainty_score":0.9987684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06095834065390648,"score_gpt":0.260629545174656,"score_spread":0.1996712045207495,"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."}}