{"id":"W4246575188","doi":"10.1515/iupac.88.0285","title":"Needle Trap","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":"Trap (plumbing); Computer science; Extraction (chemistry); Sample (material); Process engineering; Sample preparation; Microwave; Chromatography; Engineering; Chemistry; 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":[0.002039275,0.002151746,0.001782535,0.003424989,0.001088015,0.00250678,0.003721017,0.001936306,0.06768919],"category_scores_gemma":[0.009112263,0.0006402297,0.001754021,0.005289828,0.000409869,0.001655142,0.002507523,0.002036419,0.09030966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001453598,"about_ca_system_score_gemma":0.003379719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01530559,"about_ca_topic_score_gemma":0.03358199,"domain_scores_codex":[0.9979581,0.0003866408,0.0002805258,0.0007301901,0.0004281891,0.000216391],"domain_scores_gemma":[0.9967028,0.001042022,0.0005199203,0.0006906839,0.0008467498,0.0001976491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000562528,0.00005962728,0.003909655,0.005591066,0.0002460012,0.00006369941,0.00006107025,0.000685051,0.0007357354,0.001885206,0.9663921,0.01980823],"study_design_scores_gemma":[0.000291683,0.00004254086,0.004949164,0.0009169179,0.0001103982,0.00008978511,0.00006720055,0.0003830221,0.0006654282,0.002307455,0.9901412,0.00003528111],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002317726,0.000298592,0.000388819,0.0000702273,0.00002964338,0.00004433153,0.997239,0.0005438465,0.001153679],"genre_scores_gemma":[0.0005434879,0.0002927849,0.001112974,0.0001134875,0.00001077309,0.0002089125,0.9966893,0.0001223734,0.0009058464],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06768919,"threshold_uncertainty_score":0.2264429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03342215802469099,"score_gpt":0.4472424198110451,"score_spread":0.4138202617863541,"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."}}