{"id":"W2936904543","doi":"","title":"Working Across Boundaries - Follow-Up Survey","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Survey data collection; Dimension (graph theory); Work (physics); Agency (philosophy); USable; Argument (complex analysis); Public relations; Plan (archaeology); Order (exchange); Dissemination; Political science; Sociology; Computer science; World Wide Web; Business; Engineering; Geography; Social science; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.01319442,0.00009193037,0.0001952829,0.00003766839,0.002740716,0.01414173,0.002384471,0.00004091256,0.0009374975],"category_scores_gemma":[0.01028943,0.00006316017,0.0000718049,0.0001154329,0.0005074806,0.0009161123,0.001469401,0.0000706842,0.001938772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001287069,"about_ca_system_score_gemma":0.00004146958,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003759393,"about_ca_topic_score_gemma":0.06944929,"domain_scores_codex":[0.9977354,0.0001935647,0.0003659379,0.0004046254,0.0009978061,0.0003026141],"domain_scores_gemma":[0.9968818,0.0006569883,0.0002376016,0.002016276,0.0001289216,0.00007838],"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":[0.00005458559,0.00002825921,0.2159164,0.000001614608,0.00002828133,0.000008149825,0.001186799,0.00000124001,0.000004920892,0.06148238,0.459978,0.2613094],"study_design_scores_gemma":[0.0001978428,0.000007656632,0.325203,0.000003200074,0.000001550085,2.48518e-7,0.0006716349,0.00002996958,0.00003362467,0.01743615,0.6563357,0.00007941468],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5376588,0.00008256356,0.05832146,0.007742645,0.01516915,0.0003519692,0.0001859764,0.000174662,0.3803128],"genre_scores_gemma":[0.8702648,0.000004283824,0.0004021899,0.0005793428,0.00005470969,0.000003052243,0.00001155441,0.000004199308,0.1286759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.332606,"threshold_uncertainty_score":0.9999758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4935244230268321,"score_gpt":0.5068713704100264,"score_spread":0.01334694738319431,"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."}}