{"id":"W4254261091","doi":"10.1515/iupac.88.0246","title":"Solidified Floating Organic Drop","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Computer science; Extraction (chemistry); Process engineering; Drop (telecommunication); Microwave; Scale (ratio); Sample (material); Biochemical engineering; Chromatography; Chemistry; Engineering; Telecommunications; Physics","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":[0.001365451,0.002813366,0.00166699,0.004401669,0.001046347,0.002396201,0.00300528,0.00195888,0.02845011],"category_scores_gemma":[0.00450909,0.0004520236,0.001937723,0.004931504,0.0004667864,0.001191875,0.002298534,0.001752559,0.05061925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001194275,"about_ca_system_score_gemma":0.00270704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01656299,"about_ca_topic_score_gemma":0.03845638,"domain_scores_codex":[0.9982172,0.0002761435,0.0002106965,0.0005571804,0.0005049114,0.0002338333],"domain_scores_gemma":[0.9982735,0.0005007045,0.0002329255,0.0004916952,0.0003973751,0.0001038222],"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":[0.0003306046,0.0001283291,0.00312368,0.004658828,0.00021448,0.00009531616,0.00003799889,0.001710948,0.001543158,0.001129966,0.9598765,0.02715005],"study_design_scores_gemma":[0.0004252047,0.0001313704,0.008963822,0.0009173917,0.0001625129,0.0002241576,0.0001176624,0.002623664,0.003284283,0.002747474,0.9803302,0.00007226163],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007842162,0.0006712944,0.0005970744,0.00009319274,0.00006873188,0.00007289698,0.9951797,0.001035486,0.001497442],"genre_scores_gemma":[0.0009124163,0.0003216257,0.0009771016,0.00006764258,0.0000120363,0.0001486033,0.9966201,0.00005663053,0.0008838695],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02845011,"threshold_uncertainty_score":0.09517509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01914181294699167,"score_gpt":0.3819425252442061,"score_spread":0.3628007122972144,"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."}}