{"id":"W2933005044","doi":"10.7202/1060954ar","title":"Green Moments of Separation","year":2019,"lang":"en","type":"article","venue":"The Trumpeter","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Separation (statistics); Environmental science; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.000490721,0.0007203512,0.0006714154,0.001494368,0.001872558,0.002145781,0.0009274234,0.001951854,0.02235733],"category_scores_gemma":[0.00165874,0.0003941645,0.0005093074,0.000761668,0.003291168,0.002415684,0.002448025,0.004046228,0.005081057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001545427,"about_ca_system_score_gemma":0.0006634444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004078559,"about_ca_topic_score_gemma":0.0004393755,"domain_scores_codex":[0.9994024,0.0001128777,0.00001567934,0.000110924,0.0002480768,0.000110064],"domain_scores_gemma":[0.9995777,0.0001193184,0.00004142243,0.0000895889,0.0001005828,0.00007136031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004101053,0.00001293944,0.00002785087,0.00003428605,0.000003897778,0.00003655791,0.00009253649,0.000433152,0.005196889,0.9776544,0.006264548,0.0102019],"study_design_scores_gemma":[0.00002395846,0.00009883831,0.0002314291,0.00005454483,0.000009796301,0.000365014,0.0001521568,0.007880627,0.01834643,0.8309516,0.1418248,0.00006080391],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0392069,0.0109166,0.2851772,0.01931197,0.009461944,0.0001217261,0.0003940836,0.001198751,0.6342108],"genre_scores_gemma":[0.5513827,0.004665171,0.05362362,0.004343817,0.002790594,0.0001853143,0.0002086494,0.0009375279,0.3818626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02235733,"threshold_uncertainty_score":0.07479268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110530354594841,"score_gpt":0.2479914463045076,"score_spread":0.2368861427585592,"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."}}