{"id":"W3208994798","doi":"10.5281/zenodo.4587974","title":"Excel Macro for Lung Volume Recruitment Counter Data (Omega Data Logger).","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Data logger; Macro; Omega; Volume (thermodynamics); Computer science; Operating system; Programming language; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003480456,0.001323615,0.001188815,0.003022767,0.0003746402,0.00174805,0.001140306,0.000933537,0.2530706],"category_scores_gemma":[0.01476414,0.0006645179,0.0005201626,0.001968478,0.0002858265,0.001576297,0.001491871,0.001512371,0.09538662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005650317,"about_ca_system_score_gemma":0.001465329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001287889,"about_ca_topic_score_gemma":0.001351311,"domain_scores_codex":[0.9985933,0.0002851447,0.0003418776,0.0002372125,0.0004321707,0.0001103793],"domain_scores_gemma":[0.9879412,0.007827512,0.0009748972,0.001059066,0.00179497,0.0004023709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001544973,0.0002391666,0.005825211,0.001711135,0.00008440317,0.0003191285,0.0002158373,0.000746312,0.002750719,0.003546995,0.7871047,0.1959115],"study_design_scores_gemma":[0.0007638758,0.0003664536,0.02388368,0.0008550223,0.00008516682,0.0006267952,0.0002025686,0.008242667,0.01300676,0.008658138,0.9431624,0.0001464249],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.009438463,0.0007129933,0.05788769,0.001450069,0.0008045576,0.001778297,0.6691792,0.2120734,0.04667532],"genre_scores_gemma":[0.08349304,0.001496523,0.284844,0.004390298,0.000691493,0.01443028,0.4531927,0.05352649,0.1039353],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.2530706,"threshold_uncertainty_score":0.8466055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1997617354140906,"score_gpt":0.3596166926483735,"score_spread":0.159854957234283,"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."}}