{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003710931,0.0001056121,0.0001765451,0.0001486697,0.0004343629,0.00008517589,0.0005714591,0.00002502422,0.00009700602],"category_scores_gemma":[0.002482505,0.00005593018,0.00001562993,0.001483129,0.01143539,0.0005806013,0.0003040009,0.0001025599,0.00006180738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002927113,"about_ca_system_score_gemma":0.001528116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002255845,"about_ca_topic_score_gemma":0.000002692628,"domain_scores_codex":[0.9950579,0.00001184899,0.0001599027,0.0006247854,0.003452832,0.0006926765],"domain_scores_gemma":[0.9980565,0.0001243008,0.00004824937,0.0004112115,0.0005289591,0.0008308096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001221409,0.0000187999,0.1010416,0.00001353304,0.000001553359,0.00001175681,0.0003130233,1.20748e-8,0.7894772,0.001772202,0.0001190244,0.107219],"study_design_scores_gemma":[0.0009782495,0.0005065675,0.6578939,0.0003638458,0.00001435116,0.00006680063,0.0006247769,0.00009001797,0.3326258,0.0008630803,0.00581883,0.0001537514],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801359,0.0001165196,0.0002793647,0.006615579,0.0001692374,0.0002361879,0.000007930917,0.00003573983,0.01240361],"genre_scores_gemma":[0.997288,0.0001166201,0.0004961572,0.0004461228,0.0001069544,0.000007172468,1.515023e-7,0.000004019593,0.001534815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5568523,"threshold_uncertainty_score":0.9912549,"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."}}