{"id":"W6969189794","doi":"10.5281/zenodo.4413587","title":"What Are The Benefits Of Using Ciagenix Canada?","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dispose pattern; Certainty; Lift (data mining); Deferral; Upgrade; Crew","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002261505,0.000568292,0.00079827,0.001837984,0.003578701,0.006236422,0.001450419,0.004676295,0.1421806],"category_scores_gemma":[0.01218923,0.0003422388,0.0008503959,0.002231627,0.001538105,0.003294985,0.001090493,0.003856155,0.03263466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01415267,"about_ca_system_score_gemma":0.04697823,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6519552,"about_ca_topic_score_gemma":0.8646466,"domain_scores_codex":[0.9972377,0.0001941887,0.00008307507,0.0001663046,0.00195478,0.0003639414],"domain_scores_gemma":[0.9869114,0.000713645,0.0004322661,0.000128038,0.009409381,0.002405358],"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":[0.0001135479,0.00006138842,0.000919375,0.001352566,0.00002044043,0.00009830398,0.0001527571,0.00001704189,0.0002510809,0.00199749,0.8527241,0.142292],"study_design_scores_gemma":[0.00002506289,0.00003624204,0.002556489,0.001209244,0.00003240126,0.0001132517,0.0004138122,0.00001592717,0.0001925618,0.000266639,0.9951096,0.00002874743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.005179571,0.1905165,0.0007106274,0.404471,0.02060929,0.0004359184,0.009321848,0.001065714,0.3676895],"genre_scores_gemma":[0.05152411,0.2278715,0.004900717,0.2326511,0.004463549,0.0004421406,0.005557335,0.0009068926,0.4716828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3480448,"threshold_uncertainty_score":0.7001889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04755730334776849,"score_gpt":0.2427352650092993,"score_spread":0.1951779616615308,"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."}}