{"id":"W4231790755","doi":"10.1515/iupac.88.0167","title":"Active Sampling","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Computer science; Extraction (chemistry); Sampling (signal processing); Sample (material); Process engineering; Microwave; Sample preparation; Chromatography; Chemistry; Engineering; Telecommunications","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":[0.002744227,0.001710261,0.001408919,0.002777884,0.0009759984,0.002369897,0.002933812,0.001786087,0.07873333],"category_scores_gemma":[0.0174277,0.0005363331,0.001528605,0.004366651,0.0003998352,0.00164447,0.002170824,0.001700214,0.07376944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001327828,"about_ca_system_score_gemma":0.003353728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01286842,"about_ca_topic_score_gemma":0.02353265,"domain_scores_codex":[0.9969151,0.0007145905,0.0004570623,0.001111288,0.0005277392,0.0002741925],"domain_scores_gemma":[0.9938572,0.00194772,0.0007643264,0.001467147,0.001668183,0.0002954721],"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":[0.0006740226,0.00007161914,0.006629352,0.00414227,0.0002034907,0.00006243539,0.00008091337,0.0005927853,0.0004230607,0.00258429,0.9501088,0.03442692],"study_design_scores_gemma":[0.0002853163,0.00003769385,0.005895701,0.001120284,0.0001050655,0.00007543664,0.0000829383,0.0003846282,0.0005480322,0.00279736,0.9886379,0.0000297764],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000450726,0.0003640957,0.0007347322,0.0001182333,0.00007177535,0.0001190541,0.9950475,0.0004793773,0.002614656],"genre_scores_gemma":[0.001654701,0.0003207141,0.001932573,0.0002234012,0.00003469226,0.0005538856,0.992901,0.0001369912,0.002241981],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07873333,"threshold_uncertainty_score":0.2633892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04991188676580956,"score_gpt":0.4784674925996126,"score_spread":0.428555605833803,"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."}}