{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000386754,0.0005342761,0.0006826677,0.0001501384,0.0002457458,0.0002227432,0.0009200372,0.0004575702,0.00356532],"category_scores_gemma":[0.0004078477,0.0005110236,0.0001529864,0.0000964926,0.00008052883,0.0001092773,0.0001668497,0.0008897909,0.00002305004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002944152,"about_ca_system_score_gemma":0.0002140037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001142885,"about_ca_topic_score_gemma":0.0007596724,"domain_scores_codex":[0.9977332,0.00002958219,0.0005068737,0.0004233461,0.0007625227,0.0005444984],"domain_scores_gemma":[0.9979222,0.00004011277,0.0001627917,0.00145123,0.0002419809,0.0001816909],"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":[0.000008322081,0.00003215368,0.000001272903,0.0002846781,0.0001075295,0.0000907498,0.00002773313,0.0006508342,0.0002005649,0.000002470407,0.9949381,0.003655553],"study_design_scores_gemma":[0.0001946073,0.0000496488,0.00000597135,0.0003066864,0.00008344194,0.00001971593,0.00001612507,0.002259891,0.0005996551,0.00005611251,0.9958284,0.0005796836],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002111501,0.001226592,0.007721067,0.0000354784,0.001915392,0.0002121594,0.9882939,0.0002559397,0.0001282825],"genre_scores_gemma":[0.0005340995,0.001721431,0.000199114,0.00005046769,0.001152155,0.000005764774,0.9959491,0.0001066727,0.0002812352],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.007655121,"threshold_uncertainty_score":0.9997342,"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."}}