{"id":"W2542028005","doi":"10.1126/science.354.6311.427","title":"Conference navigates gap between science and government","year":2016,"lang":"en","type":"article","venue":"Science","topic":"Science, Research, and Medicine","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Waterloo","keywords":"Government (linguistics); Library science; Political science; Computer science; Philosophy; Linguistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07364978,0.001152041,0.001845273,0.003842853,0.01176664,0.02930416,0.003901658,0.02268037,0.04349938],"category_scores_gemma":[0.1566177,0.001105581,0.001556341,0.00272548,0.008206995,0.02442954,0.02920649,0.02771586,0.008590767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006580754,"about_ca_system_score_gemma":0.02249675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001464233,"about_ca_topic_score_gemma":0.002435206,"domain_scores_codex":[0.9496593,0.02579235,0.00250892,0.003370382,0.0101692,0.008499816],"domain_scores_gemma":[0.8480009,0.07676176,0.00667455,0.01257737,0.02491997,0.03106546],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004454502,0.0003032387,0.002289603,0.0007969836,0.0001541262,0.0005921292,0.006634396,0.001381715,0.000991559,0.4671056,0.4206127,0.09869251],"study_design_scores_gemma":[0.0001174328,0.0001429105,0.001481421,0.0008946139,0.00005745066,0.000160271,0.008284098,0.0009283312,0.0005225923,0.3074038,0.6799176,0.00008945106],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.008704269,0.009422399,0.01936244,0.7613708,0.0392563,0.0002094074,0.0001301879,0.0003871374,0.1611572],"genre_scores_gemma":[0.4799539,0.006157255,0.01931353,0.3051738,0.03383865,0.001016818,0.00046211,0.0007682719,0.1533156],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9263502,"threshold_uncertainty_score":0.3895019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07181230186106449,"score_gpt":0.3724404519731556,"score_spread":0.3006281501120911,"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."}}