{"id":"W6931466964","doi":"10.5281/zenodo.6132502","title":"Cliona lobata Hancock 1849","year":2014,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Shore; Lobata; Sieve (category theory); Smooth surface","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007011789,0.0001089933,0.000110602,0.000190438,0.001321549,0.001410695,0.00219821,0.00004203017,0.002752297],"category_scores_gemma":[0.0005985934,0.0001124898,0.0000402851,0.0007637717,0.00007980505,0.0005500337,0.001813554,0.0001360726,0.0151916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005174241,"about_ca_system_score_gemma":0.000003259505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004192093,"about_ca_topic_score_gemma":1.659833e-7,"domain_scores_codex":[0.9984418,0.0002640404,0.0002133693,0.0004098063,0.0003794509,0.0002915459],"domain_scores_gemma":[0.9985445,0.00002328289,0.00008625395,0.0007778007,0.0003846663,0.000183466],"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":[0.000005042452,0.0001034947,0.000004150461,0.00002400713,0.00001534841,0.000004070876,0.0003256991,0.00006927181,0.0005105781,0.3681983,0.4614692,0.1692708],"study_design_scores_gemma":[0.0002570156,0.00008987153,0.0001709226,0.00001283629,0.000003561526,0.00003299213,0.00002613924,0.03649738,0.0002197535,0.001135497,0.9614161,0.0001379185],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004575962,0.00002052101,0.836323,0.001319609,0.0001577278,0.0001325062,0.00005197738,0.001188806,0.1603482],"genre_scores_gemma":[0.9693434,0.0001659114,0.01228088,0.003754552,0.0006353344,4.456234e-8,0.003598718,0.001849892,0.008371221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9688858,"threshold_uncertainty_score":0.9999786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03815771310741739,"score_gpt":0.2706009940228726,"score_spread":0.2324432809154552,"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."}}