{"id":"W3089371172","doi":"10.1111/1740-9713.01453","title":"When Academia Meets Industry Meets Government","year":2020,"lang":"en","type":"article","venue":"Significance","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Business; Management; Engineering; Engineering management; Computer science; Economics; Philosophy","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":[],"consensus_categories":[],"category_scores_codex":[0.0001532329,0.0001190258,0.0001558748,0.000007930402,0.00006174555,0.00001965909,0.0001461959,0.000294574,0.0005279316],"category_scores_gemma":[0.0003472517,0.0001142684,0.00004111221,0.00009822621,0.00002435618,0.0001040745,0.00003664935,0.0004969107,0.00008137149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008715894,"about_ca_system_score_gemma":0.00002798366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009186858,"about_ca_topic_score_gemma":0.000003713996,"domain_scores_codex":[0.9988711,0.00004070443,0.0002695806,0.0002214158,0.0004291204,0.000168037],"domain_scores_gemma":[0.9994048,0.0001091992,0.0001585781,0.0001639349,0.00002916528,0.0001343146],"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":[0.0001580723,0.0001859332,0.03436518,0.0005279968,0.0001134522,0.00002841404,0.00749519,0.0007974477,0.02573824,0.407817,0.4884752,0.0342979],"study_design_scores_gemma":[0.00173905,0.0001900285,0.03419678,0.0002124314,0.0001452036,0.000007859534,0.0005089436,0.02654886,0.03333252,0.1051928,0.7967902,0.001135287],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7624757,0.0003592729,0.01865577,0.1702074,0.0006641402,0.001646727,0.0002200266,0.0007637669,0.04500727],"genre_scores_gemma":[0.9886384,0.00000623861,0.009072504,0.001389296,0.0002468883,0.00002125231,0.000005808705,0.00002053045,0.000599054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3083151,"threshold_uncertainty_score":0.5780481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09169510065264146,"score_gpt":0.3259053651543213,"score_spread":0.2342102645016799,"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."}}