{"id":"W6931496671","doi":"10.5281/zenodo.7003405","title":"Liopterus haemorrhoidalis","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Instar; Margin (machine learning); Table (database); Seta; Head and neck","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005758148,0.0004549868,0.0001904881,0.001075321,0.0006154232,0.0001803285,0.0002151557,0.0001421484,0.01102592],"category_scores_gemma":[0.0001629372,0.00009243849,0.0001257592,0.0004246631,0.0001785401,0.0002562384,0.0004839365,0.0001430565,0.00435753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004664055,"about_ca_system_score_gemma":0.0001596254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007704705,"about_ca_topic_score_gemma":0.01542909,"domain_scores_codex":[0.9999255,0.000006668905,0.000007093112,0.00002401467,0.00001700177,0.00001963483],"domain_scores_gemma":[0.9999191,0.000005495952,0.0000384069,0.000007426469,0.00001356502,0.00001602929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001140141,0.0001365784,0.1993034,0.001471274,0.0000962057,0.001613211,0.00242044,0.000408681,0.1248476,0.001735644,0.01869728,0.6481296],"study_design_scores_gemma":[0.00003082355,0.0005147484,0.791624,0.0002228192,0.00005315446,0.003260171,0.0009581879,0.0003137365,0.004070506,0.0002462109,0.1986842,0.00002143889],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.819934,0.007646014,0.001840599,0.0003033923,0.0001915161,0.0002338243,0.005278198,0.0003549012,0.1642176],"genre_scores_gemma":[0.9714609,0.001465923,0.002013015,0.0001356032,0.00004463321,0.00006141481,0.003360742,0.00001671898,0.02144105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01102592,"threshold_uncertainty_score":0.03688532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03224118539328549,"score_gpt":0.233640262156411,"score_spread":0.2013990767631255,"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."}}