{"id":"W1604581965","doi":"","title":"Cambridge Spurs High-Tech Growth","year":2004,"lang":"en","type":"article","venue":"Research-Technology Management","topic":"Regional Development and Policy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Phenomenon; Government (linguistics); Boom; Population; Venture capital; Agrarian society; High tech; Business; Management; Economic growth; Marketing; Economy; Engineering; Economics; Political science; Geography; Sociology; Finance; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001795071,0.0001397998,0.0001670568,0.001360179,0.001014029,0.0000915921,0.001070438,0.000249676,0.0001053407],"category_scores_gemma":[0.0002015855,0.0001401409,0.0000530345,0.00232045,0.001468831,0.0001253058,0.0005278236,0.000473878,0.001898552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006316454,"about_ca_system_score_gemma":0.0002321397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004349038,"about_ca_topic_score_gemma":0.0008210818,"domain_scores_codex":[0.9970481,0.0001504189,0.0002056873,0.0004264304,0.001089191,0.001080108],"domain_scores_gemma":[0.9991516,0.00006134441,0.00004314868,0.0003754444,0.0002140474,0.0001544429],"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":[0.000009548692,0.00007297599,0.0006161128,0.00002161733,0.00004972508,0.0001349182,0.0003287127,0.000002579422,0.00002961012,0.9645697,0.02765236,0.006512174],"study_design_scores_gemma":[0.0006125671,0.00007404893,0.006956885,0.00005616012,0.000008785611,0.000002634955,0.00293382,4.47317e-7,0.0008229871,0.3848618,0.6034445,0.0002254062],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2490178,0.0002697594,0.0003721729,0.1936471,0.0003541177,0.001164879,0.000003480872,0.000748965,0.5544218],"genre_scores_gemma":[0.9538133,0.003062144,0.002183737,0.0002471946,0.0001856799,0.0001822382,0.00000762289,0.00002180919,0.04029629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7047955,"threshold_uncertainty_score":0.9988786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06074912610846858,"score_gpt":0.4012842321249215,"score_spread":0.3405351060164529,"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."}}